Review
Computer Science, Information Systems
Merve Ozkan-Okay, Refik Samet, Omer Aslan, Deepti Gupta
Summary: This paper discusses the challenges of effectively detecting intrusions in computer networks and managing network flows to provide security. It also explores IDS components, technologies, attack nature, and tools, as well as intrusion detection technologies and recent scientific research.
Review
Computer Science, Information Systems
Arash Heidari, Mohammad Ali Jabraeil Jamali
Summary: The security of IoT involves not only network and data security but also human health and life attacks. The integration of Intrusion Detection System (IDS) with IoT systems is important for protecting system security.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Public, Environmental & Occupational Health
Faiza Akram, Dongsheng Liu, Peibiao Zhao, Natalia Kryvinska, Sidra Abbas, Muhammad Rizwan
Summary: This paper presents an approach for effective intrusion detection in the e-healthcare environment using ANFIS, addressing security challenges researchers face in maintaining patient health records in a safe IoT-net.
FRONTIERS IN PUBLIC HEALTH
(2021)
Article
Chemistry, Analytical
Mohammed M. Alani, Ali Miri
Summary: With the rapid growth of IoT devices' adoption, security has become increasingly important. In order to counter security threats, we propose an explainable and efficient method to select the most effective features for building highly accurate intrusion detection systems in IoT.
Article
Chemistry, Analytical
Gaoyuan Liu, Huiqi Zhao, Fang Fan, Gang Liu, Qiang Xu, Shah Nazir
Summary: This paper proposes a WSN intelligent intrusion detection model based on edge computing, which combines the k-Nearest Neighbor algorithm and arithmetic optimization algorithm to detect intrusions in the WSN, achieving high accuracy and performance in experimental tests.
Article
Chemistry, Multidisciplinary
Mohammed Zakariah, Abdulaziz S. Almazyad
Summary: This research focuses on the identification of anomalies in IoT systems using active learning-based algorithms. By utilizing the UNSW-NB15 dataset and combining feature engineering methods and a random forest classifier, a resilient anomaly detection model for IoT devices is constructed. The proposed model achieves an impressive accuracy rate of 99.7% and offers valuable insights and recommendations for future research in this field.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Abbas Jamalipour, Sarumathi Murali
Summary: This article investigates the comprehensive survey of intelligent intrusion detection techniques based on machine learning, deep learning, and reinforcement learning for securing IoT. It also illustrates the implementation of various categories of security threats in IoT and discusses potential research opportunities and challenges in future IoT security.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
S. Rajasoundaran, A. Prabu, Sidheswar Routray, Prince Priya Malla, G. Sateesh Kumar, Amrit Mukherjee, Yinan Qi
Summary: This paper proposes a dynamic multi-watchdog system based on deep learning, which uses DCNN and DPFES to construct a secure and cooperative multi-watchdog system, protecting each sensor node and expanding the secure medium of 5G-based IoT-WSN networks.
COMPUTER COMMUNICATIONS
(2022)
Review
Chemistry, Analytical
Khalid Albulayhi, Abdallah A. Smadi, Frederick T. Sheldon, Robert K. Abercrombie
Summary: This paper surveys deep learning approaches for intrusion detection systems in the Internet of Things, provides a comparative study of IDSs, evaluates anomaly-based IDSs on DL methods, and analyzes the performance metrics and classification algorithms. The study model shows promising outcomes for all classes of attacks in IoT ecosystems using empirically based datasets.
Review
Engineering, Electrical & Electronic
Aryan Mohammadi Pasikhani, John A. Clark, Prosanta Gope, Abdulmonem Alshahrani
Summary: The drastic reduction in manufacturing costs of sensors and actuators has led to a significant increase in the number of smart objects, which has attracted attention from cyberattackers. Researchers have proposed various security infrastructures, with Intrusion Detection Systems (IDS) playing a key role in protecting IoT networks.
IEEE SENSORS JOURNAL
(2021)
Review
Computer Science, Information Systems
Simon B. B. Weber, Stefan Stein, Michael Pilgermann, Thomas Schrader
Summary: The threat of cyber attacks in hospitals poses a risk to patient life. Medical cyber-physical systems (MCPS) are a significant source of vulnerabilities, but detecting intrusions in this environment is challenging due to device heterogeneity, diverse connectivity types, and terminology variety. Through a structured literature review, the focus was on anomaly-based detection at the network layer and detection of malicious insiders. Researchers faced a lack of suitable datasets and developed testbeds with medical devices. Future research should examine hospital networks and MCPS, create MCPS-specific datasets, support standardization, explore medical context for attack detection, and intensify efforts for intrusion prevention tailored to MCPS.
Review
Chemistry, Analytical
Muhammad Almas Khan, Muazzam A. Khan, Sana Ullah Jan, Jawad Ahmad, Sajjad Shaukat Jamal, Awais Aziz Shah, Nikolaos Pitropakis, William J. Buchanan
Summary: This paper proposed a Deep Neural Network (DNN) for intrusion detection in MQTT-based protocol and compared its performance with traditional machine learning algorithms. Results showed that the DNN model achieved high accuracy in both binary and multi-label classification on different datasets.
Article
Chemistry, Analytical
Rui Zhang, Jing Zhang, Qiqi Wang, Hehe Zhang
Summary: This paper proposes a DOIDS (Opportunistic Routing Intrusion Detection Scheme) based on the DBSCAN clustering algorithm for detecting routing attacks in UWSNs. By collecting energy consumption, forwarding, and link quality information as feature values and using the DBSCAN clustering algorithm to detect potential abnormal nodes. Finally, a decision function is defined according to the time decay function to determine whether the potential abnormal node is malicious.
Article
Computer Science, Information Systems
Alaa Alhowaide, Izzat Alsmadi, Jian Tang
Summary: This research introduces an ensemble classification model using an automatic Model Selection Method, which evaluates and selects multiple classifiers to achieve maximum accuracy with minimum false alarms in IoT networks. The proposed models outperform all others in terms of efficiency and demonstrate stable performance with a wide range of feature sets.
INTERNET OF THINGS
(2021)
Article
Computer Science, Information Systems
Gustavo A. Nunez Segura, Arsenia Chorti, Cintia Borges Margi
Summary: SDN was designed to simplify network management and automate infrastructure sharing, but its application in wireless networks may face Denial-of-Service attacks. Existing methods for detecting DoS have limitations, while our proposed lightweight online change point detector shows promising performance in addressing these challenges.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Software Engineering
Elhadj Benkhelifa, Thomas Welsh, Lo'ai Tawalbeh, Yaser Jararweh
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2020)
Article
Computer Science, Theory & Methods
Majid Al-Ruithe, Elhadj Benkhelifa
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Software Engineering
Abdelkerim Souahlia, Ammar Belatreche, Abdelkader Benyettou, Zoubir Ahmed-Foitih, Elhadj Benkhelifa, Kevin Curran
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2020)
Article
Medicine, General & Internal
Michael Edmund O'Callaghan, Jim Buckley, Brian Fitzgerald, Kevin Johnson, John Laffey, Bairbre McNicholas, Bashar Nuseibeh, Derek O'Keeffe, Ian O'Keeffe, Abdul Razzaq, Kaavya Rekanar, Ita Richardson, Andrew Simpkin, Jaynal Abedin, Cristiano Storni, Damyanka Tsvyatkova, Jane Walsh, Thomas Welsh, Liam Glynn
Summary: A national survey of the Irish population revealed a high willingness to download a contact tracing App, with main reasons being to help family and friends and a sense of responsibility to the wider community. However, concerns about privacy and data security could be critical barriers to the large-scale adoption and ongoing use of the App for effective operation.
IRISH JOURNAL OF MEDICAL SCIENCE
(2021)
Article
Medicine, General & Internal
Kaavya Rekanar, Ian R. O'Keeffe, Sarah Buckley, Manzar Abbas, Sarah Beecham, Muslim Chochlov, Brian Fitzgerald, Liam Glynn, Kevin Johnson, John Laffey, Bairbre McNicholas, Bashar Nuseibeh, James O'Connell, Derek O'Keeffe, Mike O'Callaghan, Abdul Razzaq, Ita Richardson, Andrew Simpkin, Cristiano Storni, Damyanka Tsvyatkova, Jane Walsh, Thomas Welsh, Jim Buckley
Summary: Digital contact tracing is important in reducing the spread of Covid-19, but it requires high uptake and continued participation from the population. A manual analysis of user reviews of the Irish HSE Contact Tracker app found largely positive sentiment towards the app, with suggestions for improvements such as more targeted feedback on virus incidence and more proactive engagement features. Issues with android battery and backward compatibility with older iPhones were identified as impacting app uptake.
IRISH JOURNAL OF MEDICAL SCIENCE
(2022)
Article
Telecommunications
Leila Benarous, Benamar Kadri, Ahmed Bouridane, Elhadj Benkhelifa
Summary: The existing vehicle registration systems are centralized and prone to allowing the registration of illegally smuggled or stolen vehicles. A proposed transparent system using blockchain technology aims to improve security and transparency by saving transaction records on a public blockchain. Evaluation shows the superiority of this solution compared to current registration systems in terms of security and resilience against forged transactions.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
(2022)
Article
Computer Science, Information Systems
Feras Al-Obeidat, Anoud Bani-Hani, Oluwasegun Adedugbe, Munir Majdalawieh, Elhadj Benkhelifa
Summary: Social data analysis is a vital tool for businesses and organizations to mine data from social media for purposes such as customer opinion mining, pattern recognition, and predictive analytics. This paper proposes a persistence mechanism and methodology leveraging cloud computing, microservices, and orchestration for online social data analysis, maximizing cloud capabilities and optimizing cloud computing resources to deliver real-time, persistent social data analytics as a cloud service.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Review
Health Care Sciences & Services
James O'Connell, Manzar Abbas, Sarah Beecham, Jim Buckley, Muslim Chochlov, Brian Fitzgerald, Liam Glynn, Kevin Johnson, John Laffey, Bairbre McNicholas, Bashar Nuseibeh, Michael O'Callaghan, Ian O'Keeffe, Abdul Razzaq, Kaavya Rekanar, Ita Richardson, Andrew Simpkin, Cristiano Storni, Damyanka Tsvyatkova, Jane Walsh, Thomas Welsh, Derek O'Keeffe
Summary: A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. Various considerations from the literature, such as ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation factors, were identified. Key recommendations include voluntary participation, equitable availability, user engagement strategies, adherence to data protection principles, use of Bluetooth Low Energy for contact event detection, and decentralized privacy-preserving protocols. Evaluation metrics for digital contact tracing apps were also highlighted, emphasizing the importance of transparent reporting of outcomes.
JMIR MHEALTH AND UHEALTH
(2021)
Article
Health Care Sciences & Services
Damyanka Tsvyatkova, Jim Buckley, Sarah Beecham, Muslim Chochlov, Ian R. O'Keeffe, Abdul Razzaq, Kaavya Rekanar, Ita Richardson, Thomas Welsh, Cristiano Storni
Summary: The silent transmission of COVID-19 has necessitated the use of CTAs to contain the spread, ensure public safety, and facilitate economic recovery. This study presents a comparative evaluation framework (CEF-E-3) for assessing the use of CTAs, and offers an online version for reference by developers and health authorities.
JMIR MHEALTH AND UHEALTH
(2022)
Article
Automation & Control Systems
Thomas Welsh, Faeq Alrimawi, Ali Farahani, Diane Hassett, Andrea Zisman, Bashar Nuseibeh
Summary: Supply chain fraud involving counterfeit or adulterated products poses risks to human health and safety. Inspections are crucial in mitigating fraud, and the allocation of inspection resources across geographically dispersed assets needs to consider both cost and value. However, the complexity of I4.0 environments, with their heterogeneous and dynamic cyber-physical systems, makes the analysis of inspection resource allocation computationally challenging. This article proposes a solution by using structural representations, such as supply chain and physical premises topologies, to support optimal inspection decisions in dynamic cyber-physical supply chains. The results show that this approach can significantly reduce malicious process discovery times by up to 90%.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Health Care Sciences & Services
Michael E. O'Callaghan, Manzar Abbas, Jim Buckley, Brian Fitzgerald, Kevin Johnson, John Laffey, Bairbre McNicholas, Bashar Nuseibeh, Derek O'Keeffe, Sarah Beecham, Abdul Razzaq, Kaavya Rekanar, Ita Richardson, Andrew Simpkin, James O'Connell, Cristiano Storni, Damyanka Tsvyatkova, Jane Walsh, Thomas Welsh, Liam G. Glynn
Summary: This study aims to gather public opinion on the Irish COVID Tracker digital contact tracing (DCT) App, focusing on its usage, usability, usefulness, technological issues, and potential improvements. The results show a generally positive attitude among users, with some concerns about the app's impact on phone performance. The study suggests the need for transparency on app functionality and effectiveness, as well as further research on non-users' barriers to use.
Proceedings Paper
Computer Science, Theory & Methods
Thomas Welsh, Elhadj Benkhelifa
Summary: Resilience is essential and innovative system architectures and resilience evaluation methods are needed in hostile environments. Using a novel bio-inspired fog service delivery platform, graph resilience metrics are evaluated and a new method using assortativity is proposed to assess service fluctuations.
2021 SIXTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC)
(2021)
Proceedings Paper
Automation & Control Systems
Thomas Welsh, Faeq Alrimawi, Ali Farahani, Diane Hassett, Andrea Zisman, Bashar Nuseibeh
Summary: This paper explores opportunities to engineer adaptive inspection of cyber-physical supply chains to support efforts to reduce fraud. By defining optimal inspection zones to optimize observation and reduce inspection intrusiveness, new opportunities for increased and dynamic inspection are presented.
2021 26TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA)
(2021)
Proceedings Paper
Automation & Control Systems
Ali Farahani, Liliana Pasquale, Amel Bennaceur, Thomas Welsh, Bashar Nuseibeh
Summary: This paper discusses the potential issues caused by a lack of explicit specification of fairness requirements in software systems, and proposes adaptive fairness as a solution to maintain changing fairness requirements. By combining requirements-driven and resource-driven adaptation, the approach aims to address variability in fairness requirements and associated resources.
2021 INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2021)
(2021)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Cristiano Storni, Damyanka Tsvyatkova, Ita Richardson, Jim Buckley, Manzar Abbas, Sarah Beecham, Muslim Chochlov, Brian Fitzgerald, Liam Glynn, Kevin Johnson, John Laffey, Bairbre McNicholas, Bashar Nuseibeh, James O'Connell, Derek O'Keeffe, Ian O'Keeffe, Mike O'Callaghan, Abdul Razzaq, Kaavya Rekanar, Andrew Simpkin, Jane Walsh, Thomas Welsh
Summary: This paper reports on the progress of the COVIGILANT project, focusing on the development and validation of the Usability pillar of the COVIGILANT taxonomy for Contact Tracing Applications for COVID-19. The validation process involved evaluating the Irish Health Services Executive COVID-19 CTA and 4 CTAs from other countries, leading to the completion of the Usability pillar for global CTA evaluation.
HEALTHINF: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL. 5: HEALTHINF
(2021)
