Phishing Attacks Survey: Types, Vectors, and Technical Approaches

Towards enhancing security of IoT-Enabled healthcare system

The Internet-of-Things (IoT)-based healthcare systems are comprised of a large number of networked medical devices, wearables, and sensors that collect and transmit data to improve patient care. However, the enormous number of networked devices renders these systems vulnerable to assaults. To address these challenges, researchers advocated reducing execution time, leveraging cryptographic protocols to improve security and avoid assaults, and utilizing energy-efficient algorithms to minimize energy consumption during computation. Nonetheless, these systems still struggle with long execution times, assaults, excessive energy usage, and inadequate security. We present a novel whale-based attribute encryption scheme (WbAES) that empowers the transmitter and receiver to encrypt and decrypt data using asymmetric master key encryption. The proposed WbAES employs attribute-based encryption (ABE) using whale optimization algorithm behaviour, which transforms plain data to ciphertexts and adjusts the whale fitness to generate a suitable master public and secret key, ensuring security against unauthorized access and manipulation. The proposed WbAES is evaluated using patient health record (PHR) datasets collected by IoT-based sensors, and various attack scenarios are established using Python libraries to validate the suggested framework. The simulation outcomes of the proposed system are compared to cutting-edge security algorithms and achieved finest performance in terms of reduced 11 s of execution time for 20 sensors, 0.121 mJ of energy consumption, 850 Kbps of throughput, 99.85 % of accuracy, and 0.19 ms of computational cost.

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

Open-source intelligence: a comprehensive review of the current state, applications and future perspectives in cyber security

The volume of data generated by today's digitally connected world is enormous, and a significant portion of it is publicly available. These data sources are web archives, public databases, and social networks such as Facebook, Twitter, LinkedIn, Emails, Telegrams, etc. Open-source intelligence (OSINT) extracts information from a collection of publicly available and accessible data. OSINT can provide a solution to the challenges in extracting and gathering intelligence from various publicly available information and social networks. OSINT is currently expanding at an incredible rate, bringing new artificial intelligence-based approaches to address issues of national security, political campaign, the cyber industry, criminal profiling, and society, as well as cyber threats and crimes. In this paper, we have described the current state of OSINT tools/techniques and the state of the art for various applications of OSINT in cyber security. In addition, we have discussed the challenges and future directions to develop autonomous models. These models can provide solutions for different social network-based security, digital forensics, and cyber crime-based problems using various machine learning (ML), deep learning (DL) and artificial intelligence (AI) with OSINT.

A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset.

CCrFS: Combine Correlation Features Selection for Detecting Phishing Websites Using Machine Learning

Internet users are continually exposed to phishing as cybercrime in the 21st century. The objective of phishing is to obtain sensitive information by deceiving a target and using the information for financial gain. The information may include a login detail, password, date of birth, credit card number, bank account number, and family-related information. To acquire these details, users will be directed to fill out the information on false websites based on information from emails, adverts, text messages, or website pop-ups. Examining the website’s URL address is one method for avoiding this type of deception. Identifying the features of a phishing website URL takes specialized knowledge and investigation. Machine learning is one method that uses existing data to teach machines to distinguish between legal and phishing website URLs. In this work, we proposed a method that combines correlation and recursive feature elimination to determine which URL characteristics are useful for identifying phishing websites by gradually decreasing the number of features while maintaining accuracy value. In this paper, we use two datasets that contain 48 and 87 features. The first scenario combines power predictive score correlation and recursive feature elimination; the second scenario is the maximal information coefficient correlation and recursive feature elimination. The third scenario combines spearman correlation and recursive feature elimination. All three scenarios from the combined findings of the proposed methodologies achieve a high level of accuracy even with the smallest feature subset. For dataset 1, the accuracy value for the 10 features result is 97.06%, and for dataset 2 the accuracy value is 95.88% for 10 features.

Evaluation of Contextual and Game-Based Training for Phishing Detection

Cybersecurity is a pressing matter, and a lot of the responsibility for cybersecurity is put on the individual user. The individual user is expected to engage in secure behavior by selecting good passwords, identifying malicious emails, and more. Typical support for users comes from Information Security Awareness Training (ISAT), which makes the effectiveness of ISAT a key cybersecurity issue. This paper presents an evaluation of how two promising methods for ISAT support users in acheiving secure behavior using a simulated experiment with 41 participants. The methods were game-based training, where users learn by playing a game, and Context-Based Micro-Training (CBMT), where users are presented with short information in a situation where the information is of direct relevance. Participants were asked to identify phishing emails while their behavior was monitored using eye-tracking technique. The research shows that both training methods can support users towards secure behavior and that CBMT does so to a higher degree than game-based training. The research further shows that most participants were susceptible to phishing, even after training, which suggests that training alone is insufficient to make users behave securely. Consequently, future research ideas, where training is combined with other support systems, are proposed.

How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society

Phishing attacks are on the increase. The fact that our ways of living, studying and working have drastically changed as a result of the COVID pandemic (i.e., almost everything being done online) has created many new cyber security concerns. In particular, with the move to remote working, the number of phishing emails threatening employees has increased. The 2020 Phishing Attack Landscape Report (Greathorn: 2020 Phishing attack landscape. https://info.greathorn.com/report-2020-phishing-attack-landscape/, 2020) highlights a sharp increase in the frequency of attempted phishing attacks. In this paper, we are interested in how the phishing email attack has evolved to this very threatening state. In detail, we explore the current phishing attack characteristics especially the growing challenges that have emerged as a result of the COVID-19 pandemic. The paper documents a study that presented test participants with five different categories of emails (including phishing and non phishing) . The findings from the study show that participants, generally, found it difficult to detect modern phishing email attacks. Saying that, participants were alert to the spelling mistakes of the older phishing email attacks, sensitive information being requested from them and any slight change to what they were normally used to from an email. Moreover, we have found that people were not confident, worried and often dissatisfied with the current technologies available to protect them against phishing emails. In terms of trust, these feelings alerted us to the increasing severity of the phishing attack situation and just how vulnerable society has become/ still is.

The Cybersecurity and the Care Robots: A Viewpoint on the Open Problems and the Perspectives

Care robots represent an opportunity for the health domain. The use of these robots has important implications. They can be used in surgery, rehabilitation, assistance, therapy, and other medical fields. Therefore, care robots (CR)s, have both important physical and psychological implications during their use. Furthermore, these devices, meet important data in clinical applications. These data must be protected. Therefore, cybersecurity (CS) has become a crucial characteristic that concerns all the involved actors. The study investigated the collocation of CRs in the context of CS studies in the health domain. Problems and peculiarities of these devices, with reference to the CS, were faced, investigating in different scientific databases. Highlights, ranging also from ethics implications up to the regulatory legal framework (ensuring safety and cybersecurity) have been reported. Models and cyber-attacks applicable on the CRs have been identified.

Intrusion Detection in Critical Infrastructures: A Literature Review

Over the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review on the methods of network detection and cyber attacks that can take place in a critical infrastructure. As is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions.

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.