In Situ Fabrication of Photoluminescent Hydrogen-Bonded Organic Framework-Functionalized Ca (II) Hydrogel Film for the Tetracyclines Visual Sensor and Information Security

A facilely in situ fabricated hydrogen-bonded organic framework (HOF) hydrogel film with perfect photoluminescent performance was designed for visual sensing of tetracycline antibiotics (TCs) and information security. Luminescent HOF (MA-IPA) was combined with sodium alginate (SA) through hydrogen bonding actions and electrostatic interactions, then cross-linked with Ca2+ ions to form HOF hydrogel film (Ca@MA-IPA@SA). The HOF hydrogel film exhibited exceptional mechanical robustness along with stable blue fluorescence and ultralong green phosphorescence. After exposure to TCs, Ca2+ was combined with TCs to generate a new green fluorescence exciplex (TC-Ca2+) in hydrogel films. Due to fluorescence resonance energy transfer, the fluorescence of MA-IPA was quenched, and the fluorescent color of the HOF hydrogel film was changed from blue to green. This dichromatic fluorescent response is convenient for the visual and rapid detection of TCs. The detection limits of tetracycline (TC), oxytetracycline (OTC), and chlortetracycline (CTC) were 5.1, 7.7, and 32.7 ng mL-1, respectively. Importantly, this hydrogel sensing platform was free of tedious operation and enabled the ultrasensitive and selective detection of TCs within 6 min. It has been successfully applied to TC detection in pork and milk samples. Based on the stable photoluminescence performance of HOF hydrogel films and fluorescent-responsive properties to TCs, two types of anticounterfeiting arrays were fabricated for information encryption and decryption. This work provides a novel approach for on-site detection of TCs and offers valuable insights into information security.

Field-Programmable Gate Array-Based Implementation of Zero-Trust Stream Data Encryption for Enabling 6G-Narrowband Internet of Things Massive Device Access

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor's processing time in key block generations, non-repeating checks, and data block transfers, respectively.

Qualitative study on domestic social robot adoption and associated security concerns among older adults in Slovenia

Introduction

Despite the increasing use of domestic social robots by older adults, there remains a significant knowledge gap regarding attitudes, concerns, and potential adoption behavior in this population. This study aims to categorize older adults into distinct technology adoption groups based on their attitudes toward domestic social robots and their behavior in using the existing technology.

Methods

An exploratory qualitative research design was used, involving semi-structured interviews with 24 retired Slovenian older adults aged 65 years or older, conducted between 26 June and 14 September 2023.

Results

Four distinct groups of older adults were identified: (1) Cautious Optimists, (2) Skeptical Traditionalists, (3) Positive Optimists, and (4) Technophiles based on eight characteristics.

Discussion

These groups can be aligned with the categories of the Diffusion of Innovation Theory. Privacy and security concerns, influenced by varying levels of familiarity with the technology, pose barriers to adoption. Perceived utility and ease of use vary considerably between groups, highlighting the importance of taking into account the different older adults. The role of social influence in the adoption process is complex, with some groups being more receptive to external opinions, while others exhibit more autonomous decision-making.

Assessing and Improving Data Integrity in Web-Based Surveys: Comparison of Fraud Detection Systems in a COVID-19 Study

BACKGROUND

Web-based surveys increase access to study participation and improve opportunities to reach diverse populations. However, web-based surveys are vulnerable to data quality threats, including fraudulent entries from automated bots and duplicative submissions. Widely used proprietary tools to identify fraud offer little transparency about the methods used, effectiveness, or representativeness of resulting data sets. Robust, reproducible, and context-specific methods of accurately detecting fraudulent responses are needed to ensure integrity and maximize the value of web-based survey research.

OBJECTIVE

This study aims to describe a multilayered fraud detection system implemented in a large web-based survey about COVID-19 attitudes, beliefs, and behaviors; examine the agreement between this fraud detection system and a proprietary fraud detection system; and compare the resulting study samples from each of the 2 fraud detection methods.

METHODS

The PhillyCEAL Common Survey is a cross-sectional web-based survey that remotely enrolled residents ages 13 years and older to assess how the COVID-19 pandemic impacted individuals, neighborhoods, and communities in Philadelphia, Pennsylvania. Two fraud detection methods are described and compared: (1) a multilayer fraud detection strategy developed by the research team that combined automated validation of response data and real-time verification of study entries by study personnel and (2) the proprietary fraud detection system used by the Qualtrics (Qualtrics) survey platform. Descriptive statistics were computed for the full sample and for responses classified as valid by 2 different fraud detection methods, and classification tables were created to assess agreement between the methods. The impact of fraud detection methods on the distribution of vaccine confidence by racial or ethnic group was assessed.

RESULTS

Of 7950 completed surveys, our multilayer fraud detection system identified 3228 (40.60%) cases as valid, while the Qualtrics fraud detection system identified 4389 (55.21%) cases as valid. The 2 methods showed only "fair" or "minimal" agreement in their classifications (κ=0.25; 95% CI 0.23-0.27). The choice of fraud detection method impacted the distribution of vaccine confidence by racial or ethnic group.

CONCLUSIONS

The selection of a fraud detection method can affect the study's sample composition. The findings of this study, while not conclusive, suggest that a multilayered approach to fraud detection that includes conservative use of automated fraud detection and integration of human review of entries tailored to the study's specific context and its participants may be warranted for future survey research.

The de-perimeterisation of information security: The Jericho Forum, zero trust, and narrativity

This article analyses the transformation of information security induced by the Jericho Forum, a group of security professionals who argued for a new 'de-perimeterised' security model. Having focused on defensive perimeters around networks, early 2000s information security faced a growing set of pressures: the maintainability of firewalls given increasing traffic volume and variety, the vulnerability of interior network domains, and the need to cope with and enable new working arrangements and ways of doing business. De-perimeterisation was a radical rethinking of the nature of security and created the conditions for the rise of 'Zero Trust' architectures. This shift has radical implications for the architectures of digital infrastructures that undergird many aspects of contemporary life, the risks to which people and societies are exposed, and the nature of work and business in a digital economy. We develop a semiotic analysis of the Jericho Forum's interventions. Using insights from material semiotics, security theory and the theory of narrativity, we argue that de-perimeterisation can be understood as a shift in security logic, or, a shift in how security can (be made to) make sense. We examine a cluster of images used by the Jericho Forum, and analyse how they challenged the coherence of perimeter-based thinking and provided the materials for constructing a new model. We argue that a focus on the narrative dimension of security provides a window into fundamental semantic transformations, reciprocal historical relations between semantics and technical change, the agencement of security technologies, and determinations of value (what is worth securing).

Cross Relaxation Enables Spatiotemporal Color-Switchable Upconversion in a Single Sandwich Nanoparticle for Information Security

Smart control of ionic interaction dynamics offers new possibilities for tuning and editing luminescence properties of lanthanide-based materials. However, it remains a daunting challenge to achieve the dynamic control of cross relaxation mediated photon upconversion, and in particular the involved intrinsic photophysics is still unclear. Herein, this work reports a conceptual model to realize the color-switchable upconversion of Tm3+ through spatiotemporal control of cross relaxation in the design of NaYF4 :Gd@NaYbF4 :Tm@NaYF4 sandwich nanostructure. It shows that cross relaxation plays a key role in modulating upconversion dynamics and tuning emission colors of Tm3+ . Interestingly, it is found that there is a short temporal delay for the occurrence of cross relaxation in contrast to the spontaneous emission as a result of the slight energy mismatch between relevant energy levels. This further enables a fine emission color tuning upon non-steady state excitation. Moreover, a characteristic quenching time is proposed to describe the temporal evolution of cross relaxation quantitatively. These findings present a deep insight into the physics of ionic interactions in heavy doping systems, and also show great promise in frontier applications including information security, anti-counterfeiting and nanophotonics.

Random Number Generation Based on Heterogeneous Entropy Sources Fusion in Multi-Sensor Networks

The key system serves as a vital foundation for ensuring the security of information systems. In the presence of a large scale of heterogeneous sensors, the use of low-quality keys directly impacts the security of data and user privacy within the sensor network. Therefore, the demand for high-quality keys cannot be underestimated. Random numbers play a fundamental role in the key system, guaranteeing that generated keys possess randomness and unpredictability. To address the issue of random number requirements in multi-sensor network security, this paper introduces a new design approach based on the fusion of chaotic circuits and environmental awareness for the entropy pool. By analyzing potential random source events in the sensor network, a high-quality entropy pool construction is devised. This construction utilizes chaotic circuits and sensor device awareness technology to extract genuinely random events from nature, forming a heterogeneous fusion of a high-quality entropy pool scheme. Comparatively, this proposed scheme outperforms traditional random entropy pool design methods, as it can meet the quantity demands of random entropy sources and significantly enhance the quality of entropy sources, ensuring a robust security foundation for multi-sensor networks.

Electrically function-switchable magnetic domain-wall memory

Versatile memory is strongly desired for end users, to protect their information in the information era. In particular, bit-level switchable memory that can be switched from rewritable to read-only function would allow end users to prevent important data being tampered with. However, no such switchable memory has been reported. We demonstrate that the rewritable function can be converted into read-only function by applying a sufficiently large current pulse in a U-shaped domain-wall memory, which comprises an asymmetric Pt/Co/Ru/AlOx heterostructure with strong Dzyaloshinskii-Moriya interaction. Wafer-scale switchable magnetic domain-wall memory arrays on 4-inch Si/SiO2 substrate are demonstrated. Furthermore, we confirm that the information can be stored in rewritable or read-only states at bit level according to the security needs of end users. Our work not only provides a solution for personal confidential data, but also paves the way for developing multifunctional spintronic devices.

Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center

Perimeter security in data centers helps protect systems and the data they store by preventing unauthorized access and protecting critical resources from potential threats. According to the report of the information security company SonicWall, in 2021, there was a 66% increase in the number of ransomware attacks. In addition, the message from the same company indicates that the total number of cyber threats detected in 2021 increased by 24% compared to 2019. Among these attacks, the infrastructure of data centers was compromised; for this reason, organizations include elements Physical such as security cameras, movement detection systems, authentication systems, etc., as an additional measure that contributes to perimeter security. This work proposes using artificial intelligence in the perimeter security of data centers. It allows the automation and optimization of security processes, which translates into greater efficiency and reliability in the operations that prevent intrusions through authentication, permit verification, and monitoring critical areas. It is crucial to ensure that AI-based perimeter security systems are designed to protect and respect user privacy. In addition, it is essential to regularly monitor the effectiveness and integrity of these systems to ensure that they function correctly and meet security standards.

Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding

Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security.

Demographic Comparison of Information Security Behavior Toward Health Information System Protection: Survey Study

BACKGROUND

The health information system (HIS) functions are getting wider with more diverse users. Information security in the health industry is crucial because it involves comprehensive and strategic information that might harm human life. The human factor is one of the biggest security threats to HIS.

OBJECTIVE

This study aims to investigate the information security behavior (ISB) of HIS users using a comprehensive assessment scale suited to the information security concerns in health care. Patients are increasingly being asked to submit their own data into HIS systems. As a result, this study examines the security behavior of health workers and patients, as well as their demographic variables.

METHODS

We used a quantitative approach using surveys of health workers and patients. We created a research instrument from 4 existing measurement scales to measure prosecurity and antisecurity behavior. We analyzed statistical differences to test the hypotheses, that is, the Kruskal-Wallis test and the Mann-Whitney test. The descriptive analysis was used to determine whether the group exhibited exemplary behavior when processing the survey results. A correlational test using the Spearman correlation coefficient was performed to establish the significance of the relationship between ISB and age as well as level of education.

RESULTS

We analyzed 421 responses from the survey. According to demographic factors, the hypotheses tested for full and partial security behavior reveal substantial differences. Education levels most significantly affect security behavior differences, followed by user type, gender, and age. The health workers' ISB is higher than that of the patients. Women are more likely than men to engage in prosecurity actions while avoiding antisecurity behaviors. The older the HIS user, the more likely it is that they will participate in prosecurity behavior and the less probable it is that they will engage in antisecurity behavior. According to this study, differences in prosecurity behavior are mostly impacted by education level. Higher education, on the other hand, does not guarantee improved ISB for HIS users. All demographic characteristics, particularly concerning user type, show discrepancies that are caused mainly by antisecurity behavior rather than prosecurity behavior.

CONCLUSIONS

Since patients engage in antisecurity behavior more frequently than health workers and may pose security risks, health care facilities should start to consider information security education for patients. More comprehensive research on ISB in health care facilities is required to better understand the patient's perspective, which is currently understudied.

Multimorphological Remoldable Silver Nanomaterials from Multimode and Multianalyte Colorimetric Sensing to Molecular Information Technology

Inspired by life's interaction networks, ongoing efforts are to increase complexity and responsiveness of multicomponent interactions in the system for sensing, programmable control, or information processing. Although exquisite preparation of single uniform-morphology nanomaterials has been extremely explored, the potential value of facile and one-pot preparation of multimorphology nanomaterials has been seriously ignored. Here, multimorphological silver nanomaterials (M-AgN) prepared by one pot can form interaction networks with various analytes, which can be successfully realized from multimode and multianalyte colorimetric sensing to molecular information technology (logic computing and security). The interaction of M-AgN with multianalytes not only induces multisignal responses (including color, absorbance, and wavelength shift) for sensing metal ions (Cr3+, Hg2+, and Ni2+) but also can controllably reshape its four morphologies (nanodots, nanoparticles, nanorods, and nanotriangles). By abstracting binary relationships between analytes and response signals, multicoding parallel logic operations (including simple logic gates and cascaded circuits) can be performed. In addition, taking advantage of natural concealment and molecular response characteristics of M-AgN nanosystems can also realize molecular information encoding, encryption, and hiding. This research not only promotes the construction and application of multinano interaction systems based on multimorphology and multicomponent nanoset but also provides a new imagination for the integration of sensing, logic, and informatization.

SNA: The Optimal Nodes to Raise Nurses' Infosec Awareness

Social Network Analysis (SNA) can promote Infosec awareness. A sample of 164 nurses selected the most trusted actors to get Infosec updates. UCINET 6 and NetDraw were used for mapping and PSPP 1.6.2 was used for data analysis. Nurses tend to trust managers, colleagues and IT professionals for retrieving Infosec updates.

MRNG: Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator

Privacy and security require not only strong algorithms but also reliable and readily available sources of randomness. To tackle this problem, one of the causes of single-event upsets is the utilization of a non-deterministic entropy source, specifically ultra-high energy cosmic rays. An adapted prototype based on existing muon detection technology was used as the methodology during the experiment and tested for its statistical strength. Our results show that the random bit sequence extracted from the detections successfully passed established randomness tests. The detections correspond to cosmic rays recorded using a common smartphone during our experiment. Despite the limited sample, our work provides valuable insights into the use of ultra-high energy cosmic rays as an entropy source.

Research on Energetic Micro-Self-Destruction Devices with Fast Responses

Information self-destruction devices represent the last protective net available to realize information security. The self-destruction device proposed here can generate GPa-level detonation waves through the explosion of energetic materials and these waves can cause irreversible damage to information storage chips. A self-destruction model consisting of three types of nichrome (Ni-Cr) bridge initiators with copper azide explosive elements was first established. The output energy of the self-destruction device and the electrical explosion delay time were obtained using an electrical explosion test system. The relationships between the different copper azide dosages and the assembly gap between the explosive and the target chip with the detonation wave pressure were obtained using LS-DYNA software. The detonation wave pressure can reach 3.4 GPa when the dosage is 0.4 mg and the assembly gap is 0.1 mm, and this pressure can cause damage to the target chip. The response time of the energetic micro self-destruction device was subsequently measured to be 23.65 μs using an optical probe. In summary, the micro-self-destruction device proposed in this paper offers advantages that include low structural size, fast self-destruction response times, and high energy-conversion ability, and it has strong application prospects in the information security protection field.

GLH: From Global to Local Gradient Attacks with High-Frequency Momentum Guidance for Object Detection

The adversarial attack is crucial to improving the robustness of deep learning models; they help improve the interpretability of deep learning and also increase the security of the models in real-world applications. However, existing attack algorithms mainly focus on image classification tasks, and they lack research targeting object detection. Adversarial attacks against image classification are global-based with no focus on the intrinsic features of the image. In other words, they generate perturbations that cover the whole image, and each added perturbation is quantitative and undifferentiated. In contrast, we propose a global-to-local adversarial attack based on object detection, which destroys important perceptual features of the object. More specifically, we differentially extract gradient features as a proportion of perturbation additions to generate adversarial samples, as the magnitude of the gradient is highly correlated with the model's point of interest. In addition, we reduce unnecessary perturbations by dynamically suppressing excessive perturbations to generate high-quality adversarial samples. After that, we improve the effectiveness of the attack using the high-frequency feature gradient as a motivation to guide the next gradient attack. Numerous experiments and evaluations have demonstrated the effectiveness and superior performance of our from global to Local gradient attacks with high-frequency momentum guidance (GLH), which is more effective than previous attacks. Our generated adversarial samples also have excellent black-box attack ability.

Hybrid-AI-Based iBeacon Indoor Positioning Cybersecurity: Attacks and Defenses

iBeacon systems have been increasingly established in public areas to assist users in terms of indoor location navigation and positioning. People receive the services through the Bluetooth Low Energy (BLE) installed on their mobile phones. However, the positioning and navigation functions of an iBeacon system may be compromised when faced with cyberattacks issued by hackers. In other words, its security needs to be further considered and enhanced. This study took the iBeacon system of Taipei Main Station, the major transportation hub with daily traffic of at least three hundred thousand passengers, as an example for exploring its potential attacks and further studying the defense technologies, with the assistance of AI techniques and human participation. Our experiments demonstrate that in the early stage of iBeacon system information security planning, information security technology and a rolling coding encryption should be included, representing the best defense methods at present. In addition, we believe that the adoption of rolling coding is the most cost-effective defense. However, if the security of critical infrastructure is involved, the most secure defense method should be adopted, namely a predictable and encrypted rolling coding method.

Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map

Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study's findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%.

The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers

Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks' detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.

An image cryptography method by highly error-prone DNA storage channel

Introduction: Rapid development in synthetic technologies has boosted DNA as a potential medium for large-scale data storage. Meanwhile, how to implement data security in the DNA storage system is still an unsolved problem. Methods: In this article, we propose an image encryption method based on the modulation-based storage architecture. The key idea is to take advantage of the unpredictable modulation signals to encrypt images in highly error-prone DNA storage channels. Results and Discussion: Numerical results have demonstrated that our image encryption method is feasible and effective with excellent security against various attacks (statistical, differential, noise, and data loss). When compared with other methods such as the hybridization reactions of DNA molecules, the proposed method is more reliable and feasible for large-scale applications.

Smartphone Use and Security Challenges in Hospitals: A Survey among Resident Physicians in Germany

Although mobile devices support physicians in a variety of ways in everyday clinical practice, the use of (personal) mobile devices poses potential risks for information security, data protection, and patient safety in hospitals. We used a cross-sectional survey-based study design to assess the current state of smartphone use among resident physicians in hospitals and to investigate the relationships between working conditions, current smartphone usage patterns, and security-related behavior. In total, data from 343 participating physicians could be analyzed. A large majority (98.3%) used their smartphones during clinical practice. Of the respondents who used a smartphone during clinical practice, only 4.5% were provided with a smartphone by their employer. Approximately three-quarters of the respondents who used their smartphones for professional communication never/almost never used dedicated GDPR-compliant messenger services. Using a hierarchical regression model, we found a significant effect of the organizational resources Social Support (Supervisor) and Information Security-related Communication on security-related behavior during the selection of medical apps (App Selection). Smartphones are an important part of digital support for physicians in everyday clinical practice. To minimize the risks of use, technical and organizational measures should be taken by the hospital management, resulting, for example, in a Bring-Your-Own-Device (BYOD) initiative.

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due to cyber security attacks. Therefore, there is a need for intelligent and reliable security systems that can proactively analyze the data generated by these devices and detect cybersecurity attacks. This study proposed a proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning (HVAC) attacks. Several ML algorithms were used, such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), Light Gradient Boosting (LGBM), Extreme Gradient Boosting (XGBoost), and CatBoost (CB). Furthermore, feature selection was performed using stepwise forward feature selection (FFS) technique. To alleviate the data imbalance, SMOTE and Tomeklink were used. In addition, SMOTE achieved the best results with selected features. Empirical experiments were conducted, and the results showed that the XGBoost classifier has produced the best result with 0.9999 Area Under the Curve (AUC), 0.9998, accuracy (ACC), 0.9996 Recall, 1.000 Precision and 0.9998 F1 Score got the best result. Additionally, XAI was applied to the best performing model to add the interpretability in the black-box model. Local and global explanations were generated using LIME and SHAP. The results of the proposed study have confirmed the effectiveness of ML for predicting the cyber security attacks on IoT devices and Industry 4.0.

Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

Entropy is a measure of uncertainty or randomness. It is the foundation for almost all cryptographic systems. True random number generators (TRNGs) and physical unclonable functions (PUFs) are the silicon primitives to respectively harvest dynamic and static entropy to generate random bit streams. In this survey paper, we present a systematic and comprehensive review of different state-of-the-art methods to harvest entropy from silicon-based devices, including the implementations, applications, and the security of the designs. Furthermore, we conclude the trends of the entropy source design to point out the current spots of entropy harvesting.

Über Cyber

A performative art installation in public space. Consisting only of barrier tape and the recording of the German military's “cyber march”, it invites the audience and passers-by to engage with it and fall into the trap of underestimating the amount to which it exerts force on participants.

Activating Ultrahigh Thermoresponsive Upconversion in an Erbium Sublattice for Nanothermometry and Information Security

Thermal activation of upconversion luminescence in nanocrystals opens up new opportunities in biotechnology and nanophotonics. However, it remains a daunting challenge to achieve a smart control of luminescence behavior in the thermal field with remarkable enhancement and ultrahigh sensitivity. Moreover, the physical picture involved is also debatable. Here we report a novel mechanistic design to realize an ultrasensitive thermally activated upconversion in an erbium sublattice core-shell nanostructure. By enabling a thermosensitive property into the intermediate ⁴I_11/2 level of Er³⁺ through an energy-migration-mediated surface interaction, the upconverted luminescence was markedly enhanced in the thermal field together with a striking thermochromic feature under 1530 nm irradiation. Importantly, the use of non thermally coupled red and green emissions contributes to the thermal sensitivity up to 5.27% K^-1, 3 times higher than that obtained by using conventional thermally coupled green emissions. We further demonstrate that the controllable surface interaction is a general approach to the thermal enhancement of upconversion for a series of lanthanide-based nanomaterials. Our findings pave a new way for the development of smart luminescent materials toward emerging applications such as noncontact nanothermometry, information security, and anticounterfeiting.

Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis

Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers' raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users' shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.

Development and validation of the information security attitude questionnaire (ISA-Q) for nurses

AIM

The aim of the study was to understand nurses' information security attitudes towards patient information. This study developed the Information Security Attitude Questionnaire (ISA-Q) to measure the physical, technical and administrative aspects of information security for nurses and assessed its validity and reliability.

DESIGN

Cross-sectional study and scale development.

METHODS

Exploratory and confirmatory factor analyses and correlation analyses were performed to assess construct, discriminant and convergent validity; Cronbach's α and test-retest reliability were examined.

RESULTS

Exploratory and confirmatory factor analyses yielded a 6-factor, 30-item solution. Six factors accounted for 60.19% of the total variance. The confirmatory factor analysis was achieved through structural equation modelling. Discriminant and convergent validity were confirmed. The internal consistency of the ISA-Q was 0.94, and the test-retest reliability was 0.74. The ISA-Q is an appropriate questionnaire for identifying information security attitudes of nurses, making it useful for developing systematic performance methods to enhance nurses' information security levels.

A Novel Grayscale Image Encryption Scheme Based on the Block-Level Swapping of Pixels and the Chaotic System

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.

Carbon Dots in Hydroxy Fluorides: Achieving Multicolor Long-Wavelength Room-Temperature Phosphorescence and Excellent Stability via Crystal Confinement

Carbon dots (CDs) have aroused widespread interest in the construction of room-temperature phosphorescent (RTP) materials. However, it is a great challenge to obtain simultaneous multicolor long-wavelength RTP emission and excellent stability in CD-based RTP materials. Herein, a novel and universal "CDs-in-YOHF" strategy is proposed to generate multicolor and long-wavelength RTP by confining various CDs in the Y(OH)_xF_3-x (YOHF) matrix. The mechanism of the triplet emission of CDs is related to the space confinement, the formation of hydrogen bonds and C-F bonds, and the electron-withdrawing fluorine atoms. Remarkably, the RTP lifetime of orange-emissive CDs-o@YOHF is the longest among the reported single-CD-matrix composites for emission above 570 nm. Furthermore, CDs-o@YOHF exhibited higher RTP performance at long wavelength in comparison to CDs-o@matrix (matrix = PVA, PU, urea, silica). The resulting CDs@YOHF shows excellent photostability, thermostability, chemical stability, and temporal stability, which is rather favorable for information security, especially in a complex environment.

Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Organizations responsible for seismic and volcanic monitoring worldwide mainly gather information from instrumental networks composed of specialized sensors, data-loggers, and transmission equipment. This information must be available in seismological data centers to improve early warning diffusion. Furthermore, this information is necessary for research purposes to improve the understanding of the phenomena. However, the acquisition data systems could have some information gaps due to unstable connections with instrumental networks and repeater nodes or exceeded waiting times in data acquisition processes. In this work, we performed a systematic review around information availability issues and solutions in data acquisition systems, instrumental networks, and their interplay with transmission media for seismic and volcanic monitoring. Based on the SLR methodology proposed by Kitchenham, B., a search string strategy was considered where 1938 articles were found until December 2021. Subsequently, through selection processes, 282 articles were obtained and 51 relevant articles were extracted using filters based on the content of articles mainly referring to seismic-volcanic data acquisition, data formats, monitoring networks, and early warnings. As a result, we identified two independent partial solutions that could complement each other. One focused on extracting information in the acquisition systems corresponding to continuous data generated by the monitoring points through the development of mechanisms for identifying sequential files. The other solution focused on the detection and assessment of the alternative transmission media capabilities available in the seismic-volcanic monitoring network. Moreover, we point out the advantage of a unified solution by identifying data files/plots corresponding to information gaps. These could be recovered through alternate/backup transmission channels to the monitoring points to improve the availability of the information that contributes to real-time access to information from seismic-volcanic monitoring networks, which speeds up data recovery processes.

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer's Point of View

Nowadays, the whole driver monitoring system can be placed inside the vehicle driver's smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can be crucial, leading to threat to human life and health. Moreover, despite the large number of security and privacy issues discovered in smartphone applications on a daily basis, there is no general approach for their automated analysis that can work in conditions that lack data and take into account specifics of the application area. Thus, this paper describes an original approach for a security and privacy analysis of driver monitoring systems based on smartphone sensors. This analysis uses white-box testing principles and aims to help developers evaluate and improve their products. The novelty of the proposed approach lies in combining various security and privacy analysis algorithms into a single automated approach for a specific area of application. Moreover, the suggested approach is modular and extensible, takes into account specific features of smartphone-based driver monitoring systems and works in conditions of lack or inaccessibility of data. The practical significance of the approach lies in the suggestions that are provided based on the conducted analysis. Those suggestions contain detected security and privacy issues and ways of their mitigation, together with limitations of the analysis due to the absence of data. It is assumed that such an approach would help developers take into account important aspects of security and privacy, thus reducing related issues in the developed products. An experimental evaluation of the approach is conducted on a car driver monitoring use case. In addition, the advantages and disadvantages of the proposed approach as well as future work directions are indicated.

Data Protection in Healthcare Research: Medical Students' Knowledge and Behavior

Healthcare research involves handling personal health information. Information security policies are implemented in research institutions to ensure data subjects' rights but are not always respected due to researchers' neglect or unawareness. This paper is part of an action research project at Saint Joseph University in Lebanon aiming to increase researchers' compliance with the university's information security policy. An anonymous online questionnaire was administered to medical students to evaluate their knowledge and behavior regarding patient data handling in research projects. 38 responses were collected. Results show that most students collect patient data for research, and are frequently not aware of, and do not comply with, the existing information security policy. We also found correlations between low knowledge and non-compliant behaviors including clicking on links from unknown senders, leaving computers unattended, and sharing data insecurely. To address these issues, we plan to implement various Information Security Awareness interventions and compare their effectiveness.

Classification of Challenges and Threats in Healthcare Cybersecurity: A Systematic Review

The rapid development of electronic health has highlighted the essential position information security holds today in the healthcare industry. Indeed, healthcare organisations have increasingly become targets for cyberattacks. The authors investigated cybersecurity challenges through identifying cases within the academic literature related to cybersecurity threats and vulnerabilities present within healthcare settings. The fast adoption of healthcare information systems has exposed the healthcare industry to numerous kinds of cyberattacks, thereby prompting the international academic community to investigate these threats. There are various cybersecurity challenges and vulnerabilities within healthcare that could later be exploited and lead to cybercrimes. As security systems continue to develop, the interest in cybersecurity as a form of defense and protection is expected to grow.

LPG-PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator-probabilistic context-free grammar (LPG-PCFG) based on PCFG. LPG-PCFG directionally increases the probability of low-probability passwords in the models' distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG-PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG-PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.

An Adaptive Information Security System for 5G-Enabled Smart Grid Based on Artificial Neural Network and Case-Based Learning Algorithms

With the deployment of 5G Internet of Things (IoT) in the power system, the efficiency of smart grid is improved by increasing two-way interactions in different layers in smart grid. However, it introduces more attack interfaces that the traditional information security system in smart grid cannot response in time. The neuroscience-inspired models have shown their effectiveness in solving security and optimization problems in smart grid. How to improve the security mechanism in smart grid while taking into account the optimization of data transmission efficiency using neuroscience-inspired algorithms is the problem to be solved in this study. Therefore, an information security system based on artificial neural network (ANN) and improved multiple protection model is proposed. Based on the ANN algorithm, the link state sample space is used to train the model to obtain the optimal transmission path in 5G power communication network. Integrating the intelligent link state module, the zero-trust security protection platform using case-based learning algorithm is designed and taken as the first protection, the network security logical isolation facility is taken as the second protection, and the forward and backward isolation facilities are set as the third protection to achieve the strengthened security of 5G IoT in smart grid. The experimental results show the efficiency and effectiveness of the proposed algorithms. In addition, the experimental results also show that the proposed system can resist malicious terminal access, terminal hijacking, data tampering and eavesdropping, protocol fuzzy, and denial-of-service attacks, so as to reduce the security risks of 5G IoT in smart grid. Since the proposed system can be easily integrated into the existing smart grid structure in China, the proposed system can provide a reference for the design and implementation of 5G IoT in smart grid.

Bioinspired Perovskite Nanocrystals-Integrated Photonic Crystal Microsphere Arrays for Information Security

Information security occupies an important position in the era of big data. Attempts to improve the security performance tend to impart them with more additional encryption strategies. Herein, inspired by the wettability feature of Stenocara beetle elytra and signal model of traffic light, a novel array of perovskite nanocrystals (PNs)-integrated PhC microsphere for information security is presented. The photoluminescent PNs are encapsulated in angle-independent PhC microspheres to impart them with binary optical signals as coding information. Through the multimask superposition approach, PNs-integrated PhC microspheres with different codes are placed into fluorosilane-treated PDMS substrate to form different arrays. These arrays could converge moisture on PhC microspheres in wet environment, which avoids the ions loss of the PNs and effectively prevented mutual contamination. In addition, the fluorescence of the PNs inside PhC microspheres could reversibly quench or recover in response to the environmental moisture. Based on these features, it is demonstrated that the PNs-integrated PhC microsphere arrays could realize various information encryption modes, which indicate their excellent values in information security fields.

Paper without a Trail: Time-Dependent Encryption using Pillar[5]arene-Based Host-Guest Invisible Ink

A stimuli-responsive invisible ink for time-dependent encryption of information is reported. Consisting of a pillar[5]arene-based supramolecular network grafted with spiropyran moieties, these materials display time-dependent photochromic behavior with tailorable fading rates. Ultraviolet (UV) light results in isomerization of the colorless spiropyran to the corresponding colored merocyanine, while visible light or heat causes the reverse isomerization with a rate that is dependent on the density of host-guest crosslinks. The kinetics of discoloration are a function of merocyanine aggregation, which becomes more pronounced as the host-guest crosslink density is increased, leading to a reduced conversion rate and slower time-dependent fading. The degree of crosslinking, and hence the fading rate, may be modulated via the addition of unbound pillar[5]arene host or nitrile guest as competitors. Time-dependent information encryption is enabled by combining selective placement of host and guest competitors and UV patterning. UV patterning provides an initially "false" image that does not reveal the desired information, and it is only after a given time that the encrypted data appears. This work provides a unique approach to enhance the security of information storage associated with offline portable data encryption.

A Highly Secure IoT Firmware Update Mechanism Using Blockchain

Internet of Things (IoT) device security is one of the crucial topics in the field of information security. IoT devices are often protected securely through firmware update. Traditional update methods have their shortcomings, such as bandwidth limitation and being attackers’ easy targets. Although many scholars proposed a variety of methods that are based on the blockchain technology to update the firmware, there are still demerits existing in their schemes, including large storage space and centralized stored firmware. In summary, this research proposes a highly secure and efficient protection mechanism that is based on the blockchain technology to improve the above disadvantages. Therefore, this study can reduce the need of storage space and improve system security. The proposed system has good performance in some events, including firmware integrity, security of IoT device connection, system security, and device anonymity. Furthermore, we confirm the high security and practical feasibility of the proposed system by comparing with the existing methods.

Automatic Key Update Mechanism for Lightweight M2M Communication and Enhancement of IoT Security: A Case Study of CoAP Using Libcoap Library

The ecosystem for an Internet of Things (IoT) generally comprises endpoint clients, network devices, and cloud servers. Thus, data transfers within the network present multiple security concerns. The recent boom in IoT applications has accelerated the need for a network infrastructure that provides timely and safe information exchange services. A shortcoming of many existing networks is the use of static key authentication. To enable the use of automatic key update mechanisms in IoT devices and enhance security in lightweight machine-to-machine (M2M) communications, we propose a key update mechanism, namely, double OTP (D-OTP), which combines both one-time password (OTP) and one-time pad to achieve an IoT ecosystem with theoretically unbreakable security. The proposed D-OTP was implemented into the Constrained Application Protocol (CoAP) through the commonly used libcoap library. The experimental results revealed that an additional 8.93% latency overhead was required to obtain an unbreakable guarantee of data transfers in 100 CoAP communication sessions.

A flexible and stretchable bionic true random number generator

The volume of securely encrypted data transmission increases continuously in modern society with all things connected. Towards this end, true random numbers generated from physical sources are highly required for guaranteeing security of encryption and decryption schemes for exchanging sensitive information. However, majority of true random number generators (TRNGs) are mechanically rigid, and thus cannot be compatibly integrated with some specific flexible platforms. Herein, we present a flexible and stretchable bionic TRNG inspired by the uniqueness and randomness of biological architectures. The flexible TRNG film is molded from the surface microstructures of natural plants (e.g., ginkgo leaf) via a simple, low-cost, and environmentally friendly manufacturing process. In our proof-of-principle experiment, the TRNG exhibits a fast generation speed of up to 1.04 Gbit/s, in which random numbers are fully extracted from laser speckle patterns with a high extraction rate of 72%. Significantly, the resulting random bit streams successfully pass all randomness test suites including NIST, TestU01, and DIEHARDER. Even after 10,000 times cyclic stretching or bending tests, or during temperature shock (-25-80 °C), the bionic TRNG still reveals robust mechanical reliability and thermal stability. Such a flexible TRNG shows a promising potential in information security of emerging flexible networked electronics.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material (light path diagram of transmitted laser speckle, pseudo random pattern, statistical distribution of bionic microstructures, haze of the bionic TRNG film, multi-layer circular laser intensity pattern, percentage of bit 0/1 for different hashed images, Pearson correlation coefficient between 100 different speckle images, the whole process of randomness extraction, SEM images of the flexible TRNG film after 10,000 times stretching and bending, continuous work stability of the TRNG at low or high temperature, light path diagram of reflective laser speckle, and detailed randomness test results of NIST, TestU01, and DIEHARDER) is available in the online version of this article at 10.1007/s12274-022-4109-9.

Design of Secure Microcontroller-Based Systems: Application to Mobile Robots for Perimeter Monitoring

This paper describes an original methodology for the design of microcontroller-based physical security systems and its application for the system of mobile robots. The novelty of the proposed methodology lies in combining various design algorithms on the basis of abstract and detailed system representations. The suggested design approach, which is based on the methodology, is modular and extensible, takes into account the security of the physical layer of the system, works with the abstract system representation and is looking for a trade-off between the security of the final solution and the resources expended on it. Moreover, unlike existing solutions, the methodology has a strong focus on security. It is aimed at ensuring the protection of the system against attacks at the design stage, considers security components as an integral part of the system and checks if the system can be designed in accordance with given requirements and limitations. An experimental evaluation of the methodology was conducted with help of its software implementation that consists of Python script, PostgreSQL database, Tkinter interface and available for download on our GitHub. As a use case, the system of mobile robots for perimeter monitoring was chosen. During the experimental evaluation, the design time was measured depending on the parameters of the attacker against which system security must be ensured. Moreover, the software implementation of the methodology was analyzed in compliance with requirements and compared with analogues. The advantages and disadvantages of the methodology as well as future work directions are indicated.

Luminescence Enhancement of Mn4+-Activated Fluorides via a Heterovalent Co-Doping Strategy for Monochromatic Multiplexing

Mn⁴⁺ non-equivalent doped fluorides with high color purity red emission and relatively short decay time are crucial for wide color gamut displays and emerging applications, whereas the low quantum efficiency (QE) restrains their further applications. Herein, the luminescence of Mn⁴⁺ non-equivalent doped fluoride K₂NaAlF₆:Mn⁴⁺ (KNAF:Mn⁴⁺) is significantly enhanced via a heterovalent co-doping strategy, where the luminescence intensity is obviously increased by ∼85%, but the decay time is almost unchanged. The experimental characterization and density functional theory (DFT) calculations provide an understanding of the luminescence enhancement mechanism of heterovalent co-doping, which is enabled by simultaneously improving the stability of Mn⁴⁺ and reducing the number of quenching centers (defects and impurities). Combining the short-decay-time (τ = 4.03 ms) emission KNAF:Mn⁴⁺, Mg²⁺ and long-decay-time (τ = 9.23 ms) emission K₂SiF₆:Mn⁴⁺, a novel monochromatic multiplexing mode in the millisecond order is presented, which can be decoded not only in high-efficiency by a digital camera but also with a high security. This work provides a new optical multiplexing for the information security applications and also inspires the design of high-efficiency Mn⁴⁺-activated luminescent materials.

A Novel Blockchain and Bi-Linear Polynomial-Based QCP-ABE Framework for Privacy and Security over the Complex Cloud Data

As a result of the limited resources available in IoT local devices, the large scale cloud consumer's data that are produced by IoT related machines are contracted out to the cloud. Cloud computing is unreliable, using it can compromise user privacy, and data may be leaked. Because cloud-data and grid infrastructure are both growing exponentially, there is an urgent need to explore computational sources and cloud large-data protection. Numerous cloud service categories are assimilated into numerous fields, such as defense systems and pharmaceutical databases, to compute information space and allocation of resources. Attribute Based Encryption (ABE) is a sophisticated approach which can permit employees to specify a higher level of security for data stored in cloud storage facilities. Numerous obsolete ABE techniques are practical when applied to small data sets to generate cryptograms with restricted computational properties; their properties are used to generate the key, encrypt it, and decrypt it. To address the current concerns, a dynamic non-linear polynomial chaotic quantum hash technique on top of secure block chain model can be used for enhancing cloud data security while maintaining user privacy. In the proposed method, customer attributes are guaranteed by using a dynamic non- polynomial chaotic map function for the key initialization, encryption, and decryption. In the proposed model, both organized and unorganized massive clinical data are considered to be inputs for reliable corroboration and encoding. Compared to existing models, the real-time simulation results demonstrate that the stated standard is more precise than 90% in terms of bit change and more precise than 95% in terms of dynamic key generation, encipherment, and decipherment time.

Deceptive Techniques to Hide a Compressed Video Stream for Information Security

With the recent development of video compression methods, video transmission on traditional devices and video distribution using networks has increased in various devices such as drones, IP cameras, and small IoT devices. As a result, the demand for encryption techniques such as MPEG-DASH for transmitting streams over networks is increasing. These video stream security methods guarantee stream confidentiality. However, they do not hide the fact that the encrypted stream is being transmitted over the network. Considering that sniffing attacks can analyze the entropy of the stream and scan huge amounts of traffic on the network, to solve this problem, the deception method is required, which appears unencrypted but a confidential stream. In this paper, we propose the new deception method that utilizes standard NAL unit rules of video codec, where the unpromised device shows the cover video and the promised device shows the secret video for deceptive security. This method allows a low encryption cost and the stream to dodge entropy-based sniffing scan attacks. The proposed stream shows that successful decoding using five standard decoders and processing performance was 61% faster than the conventional encryption method in the test signal conformance set. In addition, a network encrypted stream scan method the HEDGE showed classification results that our stream is similar to a compressed video.

A Secure Communication System for Constrained IoT Devices-Experiences and Recommendations

The Internet of Things networks connect a large number of devices and can be used for various purposes. IoT systems collect and process vast amounts of often sensitive data. Information security should be the key feature of an IoT network. In this paper, we present the IoT-Crypto—secure communication system for the Internet of Things. It addresses IoT features, such as constrained abilities of devices, needs to reduce the volume of the transmitted data and be compatible with the Internet. IoT-Crypto introduces an innovative, lightweight certificate format and trust model based on real-world business relations. It also specifies secure communication protocol, which uses underlying encrypted DTLS connection. This paper presents IoT-Crypto in the context of comparable solutions, discusses its distinctive features and implementation details. Results of tests and experiments performed in the IoT-Crypto network confirm that it works correctly and securely. Test network was also used to ascertain the suitability of encoding standards and BLE IPSP profile for the IoT. Directions of future work were discussed based on those results.

Sensors for Context-Aware Smart Healthcare: A Security Perspective

The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people's welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

The Role of User Behaviour in Improving Cyber Security Management

Information security has for long time been a field of study in computer science, software engineering, and information communications technology. The term 'information security' has recently been replaced with the more generic term cybersecurity. The goal of this paper is to show that, in addition to computer science studies, behavioural sciences focused on user behaviour can provide key techniques to help increase cyber security and mitigate the impact of attackers' social engineering and cognitive hacking methods (i.e., spreading false information). Accordingly, in this paper, we identify current research on psychological traits and individual differences among computer system users that explain vulnerabilities to cyber security attacks and crimes. Our review shows that computer system users possess different cognitive capabilities which determine their ability to counter information security threats. We identify gaps in the existing research and provide possible psychological methods to help computer system users comply with security policies and thus increase network and information security.

Mapping the Psychosocialcultural Aspects of Healthcare Professionals' Information Security Practices: Systematic Mapping Study

Background

Data breaches in health care are on the rise, emphasizing the need for a holistic approach to mitigation efforts.

Objective

The purpose of this study was to develop a comprehensive framework for modeling and analyzing health care professionals’ information security practices related to their individual characteristics, such as their psychological, social, and cultural traits.

Methods

The study area was a hospital setting under an ongoing project called the Healthcare Security Practice Analysis, Modeling, and Incentivization (HSPAMI) project. A literature review was conducted for relevant theories and information security practices. The theories and security practices were used to develop an ontology and a comprehensive framework consisting of psychological, social, cultural, and demographic variables.

Results

In the review, a number of psychological, social, and cultural theories were identified, including the health belief model, protection motivation theory, theory of planned behavior, and social control theory, in addition to some social demographic variables, to form a comprehensive set of health care professionals’ characteristics. Furthermore, an ontology was developed from these theories to systematically organize the concepts. The framework, called the psychosociocultural (PSC) framework, was then developed from the various combined psychological and sociocultural attributes of the ontology. The Human Aspect of Information Security Questionnaire was adopted as a comprehensive tool for gathering staff security practices as mediating variables in the framework.

Conclusions

Data breaches occur often in health care today. This frequency has been attributed to the lack of experience of health care professionals in information security, the lack of development of conscious care security practices, and the lack of motivation to incentivize health care professionals. The frequent data breaches in health care threaten the mutual trust between health care professionals and patients, which implicitly impacts the quality of the health care service. The modeling and analysis of health care professionals’ security practices can be conducted with the PSC framework by combining methods of statistical survey, observations, and interviews in relation to PSC variables, such as perceptions (perceived benefits, perceived threats, and perceived barriers) or psychological traits, social factors, cultural factors, and social demographics.

Non-Linear Hopped Chaos Parameters-Based Image Encryption Algorithm Using Histogram Equalization

Multimedia wireless communications have rapidly developed over the years. Accordingly, an increasing demand for more secured media transmission is required to protect multimedia contents. Image encryption schemes have been proposed over the years, but the most secure and reliable schemes are those based on chaotic maps, due to the intrinsic features in such kinds of multimedia contents regarding the pixels’ high correlation and data handling capabilities. The novel proposed encryption algorithm introduced in this article is based on a 3D hopping chaotic map instead of fixed chaotic logistic maps. The non-linearity behavior of the proposed algorithm, in terms of both position permutation and value transformation, results in a more secured encryption algorithm due to its non-convergence, non-periodicity, and sensitivity to the applied initial conditions. Several statistical and analytical tests such as entropy, correlation, key sensitivity, key space, peak signal-to-noise ratio, noise attacks, number of pixels changing rate (NPCR), unified average change intensity randomness (UACI), and others tests were applied to measure the strength of the proposed encryption scheme. The obtained results prove that the proposed scheme is very robust against different cryptography attacks compared to similar encryption schemes.

Security Analysis and Improvement of an Image Encryption Cryptosystem Based on Bit Plane Extraction and Multi Chaos

This paper analyzes the security of image encryption systems based on bit plane extraction and multi chaos. It includes a bit-level permutation for high, 4-bit planes and bit-wise XOR diffusion, and finds that the key streams in the permutation and diffusion phases are independent of the plaintext image. Therefore, the equivalent diffusion key and the equivalent permutation key can be recovered by the chosen-plaintext attack method, in which only two special plaintext images and their corresponding cipher images are used. The effectiveness and feasibility of the proposed attack algorithm is verified by a MATLAB 2015b simulation. In the experiment, all the key streams in the original algorithm are cracked through two special plaintext images and their corresponding ciphertext images. In addition, an improved algorithm is proposed. In the improved algorithm, the generation of a random sequence is related to ciphertext, which makes the encryption algorithm have the encryption effect of a “one time pad”. The encryption effect of the improved algorithm is better than that of the original encryption algorithm in the aspects of information entropy, ciphertext correlation analysis and ciphertext sensitivity analysis.