1
|
Aldaej A, Ahanger TA, Ullah I. Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments. Sensors (Basel) 2023; 23:9869. [PMID: 38139716 PMCID: PMC10747713 DOI: 10.3390/s23249869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge-cloud deployment. A new edge-cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge-cloud deployment.
Collapse
Affiliation(s)
- Abdulaziz Aldaej
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Tariq Ahamed Ahanger
- Department of Management Information Systems, College of Business Administration (CoBA), Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Imdad Ullah
- School of Computer Science, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia;
| |
Collapse
|
2
|
S. B, G. L, Vaiyapuri T, Ahanger TA, Dahan F, Hajjej F, Keshta I, Alsafyani M, Alroobaea R, Raahemifar K. A convolutional neural network for face mask detection in IoT-based smart healthcare systems. Front Physiol 2023; 14:1143249. [PMID: 37064899 PMCID: PMC10102606 DOI: 10.3389/fphys.2023.1143249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/16/2023] [Indexed: 04/03/2023] Open
Abstract
The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.
Collapse
Affiliation(s)
- Bose S.
- Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India
- *Correspondence: Bose S.,
| | - Logeswari G.
- Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India
| | - Thavavel Vaiyapuri
- Department of Computer Sciences, College of Computer engineering and sciences, Prince Sattam Bin AbdulAziz University, Al-Kharj, Saudi Arabia
| | - Tariq Ahamed Ahanger
- Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Fadl Dahan
- Department of Management Information Systems, College of Business Administration Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Fahima Hajjej
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Al-Hawiyya, Saudi Arabia
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, Al-Hawiyya, Saudi Arabia
| | - Kaamran Raahemifar
- College of Information Sciences and Technology, Data Science and Artificial Intelligence Program, State College, Penn State University, State College, PA, United States
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, Waterloo, ON, Canada
- Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| |
Collapse
|
3
|
Bhatia M, Manocha A, Ahanger TA, Alqahtani A. Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control. Artif Intell Med 2022; 127:102288. [PMID: 35430039 PMCID: PMC8956352 DOI: 10.1016/j.artmed.2022.102288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/19/2022] [Accepted: 03/22/2022] [Indexed: 12/18/2022]
Abstract
COVID-19 is a life-threatening contagious virus that has spread across the globe rapidly. To reduce the outbreak impact of COVID-19 virus illness, continual identification and remote surveillance of patients are essential. Medical service delivery based on the Internet of Things (IoT) technology backed up by the fog-cloud paradigm is an efficient and time-sensitive solution for remote patient surveillance. Conspicuously, a comprehensive framework based on Radio Frequency Identification Device (RFID) and body-wearable sensor technologies supported by the fog-cloud platform is proposed for the identification and management of COVID-19 patients. The J48 decision tree is used to assess the infection degree of the user based on corresponding symptoms. RFID is used to detect Temporal Proximity Interactions (TPI) among users. Using TPI quantification, Temporal Network Analysis is used to analyze and track the current stage of the COVID-19 spread. The statistical performance and accuracy of the framework are assessed by utilizing synthetically-generated data for 250,000 users. Based on the comparative analysis, the proposed framework acquired an enhanced measure of classification accuracy, and sensitivity of 96.68% and 94.65% respectively. Moreover, significant improvement has been registered for proposed fog-cloud-based data analysis in terms of Temporal Delay efficacy, Precision, and F-measure.
Collapse
Affiliation(s)
- Munish Bhatia
- Department of Computer Science and Engineering, Lovely Professional University, India.
| | - Ankush Manocha
- Department of Computer Applications, Lovely Professional University, India
| | - Tariq Ahamed Ahanger
- College of Computer Engineering and Science, Prince Sattam Bin ABdulaziz University, Al-Kharj, Saudi Arabia.
| | - Abdullah Alqahtani
- College of Computer Engineering and Science, Prince Sattam Bin ABdulaziz University, Al-Kharj, Saudi Arabia.
| |
Collapse
|
4
|
Aldaej A, Ahanger TA, Atiquzzaman M, Ullah I, Yousufudin M. Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective. Sensors 2022; 22:s22072630. [PMID: 35408244 PMCID: PMC9002915 DOI: 10.3390/s22072630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 02/01/2023]
Abstract
Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).
Collapse
Affiliation(s)
- Abdulaziz Aldaej
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia; (T.A.A.); (I.U.); (M.Y.)
- Correspondence:
| | - Tariq Ahamed Ahanger
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia; (T.A.A.); (I.U.); (M.Y.)
| | | | - Imdad Ullah
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia; (T.A.A.); (I.U.); (M.Y.)
| | - Muhammad Yousufudin
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia; (T.A.A.); (I.U.); (M.Y.)
| |
Collapse
|
5
|
Ahanger TA, Tariq U, Nusir M, Aldaej A, Ullah I, Sulman A. A novel IoT-fog-cloud-based healthcare system for monitoring and predicting COVID-19 outspread. J Supercomput 2022; 78:1783-1806. [PMID: 34177116 PMCID: PMC8215493 DOI: 10.1007/s11227-021-03935-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 05/04/2023]
Abstract
Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial-temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model.
Collapse
Affiliation(s)
- Tariq Ahamed Ahanger
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | - Usman Tariq
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | - Muneer Nusir
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | - Abdulaziz Aldaej
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | - Imdad Ullah
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | | |
Collapse
|
6
|
Nusir M, Tariq U, Ahanger TA. Engaging Diverse Stakeholders in Interdisciplinary Co-Design Project for Better Service Design. Journal of Cases on Information Technology 2021. [DOI: 10.4018/jcit.296253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The rapid development of information technology (IT) has enabled digital services to evolve continually and support a growing number of internet-enabled devices, along with user diversity. The end-user anticipation within the smart environments, which are internet-enabled, delivery networks and innovative technologies. What tools/methods can support the collaborative design and effectively choreograph the design process with dynamic knowledge between service designers and service users? The cooperative design is recognizable in the design environment with a collection wide-ranged by co-design methods and tools. In-depth interviews uncover contextually appropriate design process requirements from diverse stakeholder groups. A collection of design tools and methods are selected and implemented within a Web-based co-design platform. Uncovered design requirements are subsequently applied in extending the Double Diamond framework prior to operationalization into a design process blueprint with supporting service design tool selection as the main contributions for this paper.
Collapse
Affiliation(s)
- Muneer Nusir
- Prince Sattam bin Abdulaziz University, Saudi Arabia
| | - Usman Tariq
- Prince Sattam bin Abdulaziz University, Saudi Arabia
| | | |
Collapse
|
7
|
Savaliya A, Jhaveri RH, Xin Q, Alqithami S, Ramani S, Ahanger TA. Securing industrial communication with software-defined networking. Math Biosci Eng 2021; 18:8298-8313. [PMID: 34814300 DOI: 10.3934/mbe.2021411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91-99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.
Collapse
Affiliation(s)
- Abhishek Savaliya
- Department of Computer Science and Engineering, Pandit Deendayal Energy University, India
| | - Rutvij H Jhaveri
- Department of Computer Science and Engineering, Pandit Deendayal Energy University, India
| | - Qin Xin
- Faculty of Science and Technology University of the Faroe Islands Vestarabryggja 15, FO 100 Torshavn, Faroe Islands, Denmark
| | - Saad Alqithami
- Department of Computer Science, Albaha University, Saudi Arabia
| | | | - Tariq Ahamed Ahanger
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Saudi Arabia
| |
Collapse
|
8
|
Abstract
In this paper, a robotic system dedicated to remote wrist rehabilitation is proposed as an Internet of Things (IoT) application. The system offers patients home rehabilitation. Since the physiotherapist and the patient are on different sites, the system guarantees that the physiotherapist controls and supervises the rehabilitation process and that the patient repeats the same gestures made by the physiotherapist. A human-machine interface (HMI) has been developed to allow the physiotherapist to remotely control the robot and supervise the rehabilitation process. Based on a computer vision system, physiotherapist gestures are sent to the robot in the form of control instructions. Wrist range of motion (RoM), EMG signal, sensor current measurement, and streaming from the patient’s environment are returned to the control station. The various acquired data are displayed in the HMI and recorded in its database, which allows later monitoring of the patient’s progress. During the rehabilitation process, the developed system makes it possible to follow the muscle contraction thanks to an extraction of the Electromyography (EMG) signal as well as the patient’s resistance thanks to a feedback from a current sensor. Feature extraction algorithms are implemented to transform the EMG raw signal into a relevant data reflecting the muscle contraction. The solution incorporates a cascade fuzzy-based decision system to indicate the patient’s pain. As measurement safety, when the pain exceeds a certain threshold, the robot should stop the action even if the desired angle is not yet reached. Information on the patient, the evolution of his state of health and the activities followed, are all recorded, which makes it possible to provide an electronic health record. Experiments on 3 different subjects showed the effectiveness of the developed robotic solution.
Collapse
Affiliation(s)
- Yassine Bouteraa
- Digital Research Center of Sfax & CEM Lab-ENIS, University of Sfax, Sfax, Tunisia
- Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Atef Ibrahim
- Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | |
Collapse
|