Outputs (412)

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.

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.

Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode (2024)
Presentation / Conference Contribution
Buchanan, W., Grierson, S., & Uribe, D. (2024, February). Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode. Presented at 10th International Conference on Information Systems Security and Privacy, Rome, Italy

Biometric data is often highly sensitive, and a leak of this data can lead to serious privacy breaches. Some of the most sensitive of this type of data relates to the usage of DNA data on individuals. A leak of this type of data without consent could... Read More about Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode.

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.

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.

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. (online). Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles. IEEE Transactions on Intelligent Transportation Systems, 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.

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.

Exploring DTrace as an Incident Response Tool for Unix Systems (2024)
Presentation / Conference Contribution
Duin, J., Mckeown, S., & Abubakar, M. (2024, June). Exploring DTrace as an Incident Response Tool for Unix Systems. Presented at Cyber Science 2024, Edinburgh, Scotland

Critical National Infrastructure (CNI) is often the target of sophisticated and sustained cyber attacks perpetrated by advanced threat actors with considerable resources. These attacks can lead to interruptions in core services such as energy and wa... Read More about Exploring DTrace as an Incident Response Tool for Unix Systems.

OPSEC VS Leaked Credentials: Password reuse in Large-Scale Data Leaks (2023)
Presentation / Conference Contribution
Uzonyi, D. G., Pitropakis, N., McKeown, S., & Politis, I. (2023, November). OPSEC VS Leaked Credentials: Password reuse in Large-Scale Data Leaks. Presented at 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Edinburgh, UK

Security and authentication are ubiquitous problems that impact all modern networked systems. Password-based authentication systems are still prevalent, and information leaked via other channels may be used to attack networked systems. Researchers ha... Read More about OPSEC VS Leaked Credentials: Password reuse in Large-Scale Data Leaks.

An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples (2021)
Presentation / Conference Contribution
Verdi, M., Sami, A., Akhondali, J., Khomh, F., Uddin, G., & Karami Motlagh, A. (2021, May). An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples. Presented at 43rd International Conference on Software Engineering, Online

Software developers share programming solutions in Q&A sites like Stack Overflow, Stack Exchange, Android forum, and so on. The reuse of crowd-sourced code snippets can facilitate rapid prototyping. However, recent research shows that the shared code... Read More about An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples.

Intrusion Detection, Measurement Correction, and Attack Localization of PMU Networks (2021)
Journal Article
Khalafi, Z. S., Dehghani, M., Khalili, A., Sami, A., Vafamand, N., & Dragicevic, T. (2022). Intrusion Detection, Measurement Correction, and Attack Localization of PMU Networks. IEEE Transactions on Industrial Electronics, 69(5), 4697-4706. https://doi.org/10.1109/tie.2021.3080212

Accurate state estimation is essential for correct supervision of power grids. With the existence of cyber-attacks, state estimation may become inaccurate, which can eventually lead to wrong supervisory decision making. To detect cyber-attacks in pow... Read More about Intrusion Detection, Measurement Correction, and Attack Localization of PMU Networks.