Outputs (426)

IoT Authentication Protocols: Challenges, and Comparative Analysis (2024)
Journal Article
Alsheavi, A., Hawbani, A., Othman, W., Wang, X., Qaid, G. R. S., Zhao, L., Al-Dubai, A., Liu, Z., Ismail, A., Haveri, R. H., Alsamhi, S. H., & Al-Qaness, M. A. A. (in press). IoT Authentication Protocols: Challenges, and Comparative Analysis. ACM computing surveys,

In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a sub... Read More about IoT Authentication Protocols: Challenges, and Comparative Analysis.

BOAZ, Yet Another Layered Evasion Tool: Evasion Tool Evaluations and AV Testing (2024)
Presentation / Conference Contribution
Macfarlane, R., & Xuan Meng, T. (2024, August). BOAZ, Yet Another Layered Evasion Tool: Evasion Tool Evaluations and AV Testing. Presented at blackhat USA 2024, Las Vegas, US

In the rapidly evolving landscape of cybersecurity, there has been an increasing deployment of evasion techniques in organizational vulnerability assessments and found post-discovery of security incidents, owing to the more sophisticated defense mech... Read More about BOAZ, Yet Another Layered Evasion Tool: Evasion Tool Evaluations and AV Testing.

Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System (2024)
Journal Article
Cheng, H., Tan, Z., Zhang, X., & Liu, Y. (online). Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System. Chinese Journal of Electronics, https://doi.org/10.23919/cje.2023.00.012

Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been... Read More about Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System.

Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles (2024)
Journal Article
Zhao, L., Qian, H., Hawbani, A., Al-Dubai, A. Y., Tan, Z., Yu, K., & Zomaya, A. Y. (2024). Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles. IEEE Transactions on Intelligent Transportation Systems, 25(10), 15065-15080. https://doi.org/10.1109/TITS.2024.3398602

Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fost... Read More about Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles.

MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer (2024)
Journal Article
Wang, F., Shi, X., Yang, F., Song, R., Li, Q., Tan, Z., & Wang, C. (2024). MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer. Journal of Information Security and Applications, 83, Article 103784. https://doi.org/10.1016/j.jisa.2024.103784

The proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications. The accurate and effective classification makes a pivotal role in defend... Read More about MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer.

Detection of Ransomware (2024)
Patent
Buchanan, B., McLaren, P., Russell, G., & Tan, Z. (2024). Detection of Ransomware. US20240152616A1

The present invention relates to a computer program product, a computing device and a method of detecting a file encrypted by ransomware by identifying a file write operation for a file on the computing device and determining if a predetermined numbe... Read More about Detection of Ransomware.

Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks (2024)
Journal Article
Bhatti, D. S., Saleem, S., Ali, Z., Park, T., Suh, B., Kamran, A., Buchanan, W. J., & Kim, K. (2024). Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks. IEEE Access, 12, 41499-41516. https://doi.org/10.1109/access.2024.3377144

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head select... Read More about Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks.

SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT (2024)
Journal Article
Alshehri, M. S., Ahmad, J., Almakdi, S., Qathrady, M. A., Ghadi, Y. Y., & Buchanan, W. J. (2024). SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT. IEEE Access, 12, https://doi.org/10.1109/access.2024.3371992

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweigh... Read More about SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT.

STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation (2024)
Journal Article
Fang, M., Yu, L., Xie, H., Tan, Q., Tan, Z., Hussain, A., Wang, Z., Li, J., & Tian, Z. (2024). STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation. IEEE Transactions on Computational Social Systems, 11(4), 5354 - 5366. https://doi.org/10.1109/tcss.2024.3356549

The impressive development of facial manipulation techniques has raised severe public concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as one of promising face forgery detection approaches with additional ref... Read More about STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation.

Machine Un-learning: An Overview of Techniques, Applications, and Future Directions (2023)
Journal Article
Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., & Hussain, A. (2024). Machine Un-learning: An Overview of Techniques, Applications, and Future Directions. Cognitive Computation, 16, 482-506. https://doi.org/10.1007/s12559-023-10219-3

ML applications proliferate across various sectors. Large internet firms employ ML to train intelligent models using vast datasets, including sensitive user information. However, new regulations like GDPR require data removal by businesses. Deleting... Read More about Machine Un-learning: An Overview of Techniques, Applications, and Future Directions.

Hybrid Threats, Cyberterrorism and Cyberwarfare (2023)
Book
Ferrag, M. A., Kantzavelou, I., Maglaras, L., & Janicke, H. (Eds.). (2024). Hybrid Threats, Cyberterrorism and Cyberwarfare. Boca Raton: CRC Press. https://doi.org/10.1201/9781003314721

Nowadays in cyberspace, there is a burst of information to which everyone has access. However, apart from the advantages the internet offers, it also hides numerous dangers for both people and nations. Cyberspace has a dark side, including terrorism,... Read More about Hybrid Threats, Cyberterrorism and Cyberwarfare.

Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection (2023)
Journal Article
Manikandaraja, A., Aaby, P., & Pitropakis, N. (2023). Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection. Computers, 12(10), Article 195. https://doi.org/10.3390/computers12100195

Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional... Read More about Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection.

A Blockchain-based two Factor Honeytoken Authentication System (2023)
Presentation / Conference Contribution
Papaspirou, V., Maglaras, L., Kantzavelou, I., Moradpoor, N., & Katsikas, S. (2023, September). A Blockchain-based two Factor Honeytoken Authentication System . Poster presented at 28th European Symposium on Research in Computer Security (ESORICS), The Hague

This paper extends and advances our recently introduced two-factor Honeytoken authentication method by incorporating blockchain technology. This novel approach strengthens the authentication method, preventing various attacks, including tampering att... Read More about A Blockchain-based two Factor Honeytoken Authentication System.

Start thinking in graphs: using graphs to address critical attack paths in a Microsoft cloud tenant (2023)
Journal Article
Elmiger, M., Lemoudden, M., Pitropakis, N., & Buchanan, W. J. (2024). Start thinking in graphs: using graphs to address critical attack paths in a Microsoft cloud tenant. International Journal of Information Security, 23, 467-485. https://doi.org/10.1007/s10207-023-00751-6

The challenge of securing IT environments has reached a new complexity level as a growing number of organisations adopt cloud solutions. This trend increases the possibility of overseen attack paths in an organisation’s IT infrastructure. This paper... Read More about Start thinking in graphs: using graphs to address critical attack paths in a Microsoft cloud tenant.

Use and operational safety (2023)
Book Chapter
Reed, N., Charisis, V., & Cowper, S. (2023). Use and operational safety. In D. Ventriglia, & M. Kahl (Eds.), FISITA Intelligent Safety White Paper – The Safety of Electro-Mobility: Expert considerations on the Safety of an Electric Vehicle from concept through end of life (107-111). FISITA

Whether you are an individual buying your first car or replacing an existing vehicle, or if you are a fleet manager making vehicle purchase decisions on behalf of a company, the acquisition of a car is usually a highly significant purchase. Increasi... Read More about Use and operational safety.

A stacking ensemble of deep learning models for IoT intrusion detection (2023)
Journal Article
Lazzarini, R., Tianfield, H., & Charissis, V. (2023). A stacking ensemble of deep learning models for IoT intrusion detection. Knowledge-Based Systems, 279, Article 110941. https://doi.org/10.1016/j.knosys.2023.110941

The number of Internet of Things (IoT) devices has increased considerably in the past few years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a consequence, the prompt detection of attacks in IoT environments thr... Read More about A stacking ensemble of deep learning models for IoT intrusion detection.

Majority Voting Ransomware Detection System (2023)
Journal Article
Davies, S. R., Macfarlane, R., & Buchanan, W. J. (2023). Majority Voting Ransomware Detection System. Journal of Information Security, 14(4), 264-293. https://doi.org/10.4236/jis.2023.144016

Crypto-ransomware remains a significant threat to governments and companies alike, with high-profile cyber security incidents regularly making headlines. Many different detection systems have been proposed as solutions to the ever-changing dynamic la... Read More about Majority Voting Ransomware Detection System.

Federated Learning for IoT Intrusion Detection (2023)
Journal Article
Lazzarini, R., Tianfield, H., & Charissis, V. (2023). Federated Learning for IoT Intrusion Detection. Artificial Intelligence, 4(3), 509-530. https://doi.org/10.3390/ai4030028

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems... Read More about Federated Learning for IoT Intrusion Detection.

A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection (2023)
Preprint / Working Paper
Lazzarini, R., Tianfield, H., & Charissis, V. A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection

The number of Internet of Things (IoT) devices has increased considerably inthe past few years, which resulted in an exponential growth of cyber attackson IoT infrastructure. As a consequence, the prompt detection of attacks inIoT environments throug... Read More about A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection.

Hamming Distributions of Popular Perceptual Hashing Techniques (2023)
Journal Article
McKeown, S., & Buchanan, W. J. (2023). Hamming Distributions of Popular Perceptual Hashing Techniques. Forensic Science International: Digital Investigation, 44(Supplement), Article 301509. https://doi.org/10.1016/j.fsidi.2023.301509

Content-based file matching has been widely deployed for decades, largely for the detection of sources of copyright infringement, extremist materials, and abusive sexual media. Perceptual hashes, such as Microsoft's PhotoDNA, are one automated mechan... Read More about Hamming Distributions of Popular Perceptual Hashing Techniques.