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Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode (2024)
Conference Proceeding
Buchanan, W., Grierson, S., & Uribe, D. (2024). Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode. In Proceedings of the 10th International Conference on Information Systems Security and Privacy (226-233). https://doi.org/10.5220/0012454300003648

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.

A Probability Mapping-Based Privacy Preservation Method for Social Networks (2024)
Conference Proceeding
Li, Q., Wang, Y., Wang, F., Tan, Z., & Wang, C. (2024). A Probability Mapping-Based Privacy Preservation Method for Social Networks. . https://doi.org/10.1007/978-981-97-1274-8_19

The mining and analysis of social networks can bring significant economic and social benefits. However, it also poses a risk of privacy leakages. Differential privacy is a de facto standard to prevent such leaks, but it suffers from the high sensitiv... Read More about A Probability Mapping-Based Privacy Preservation Method for Social 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. (in press). SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT. IEEE Access, 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.

Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System (2024)
Journal Article
Cheng, H., Tan, Z., Zhang, X., & Liu, Y. (in press). Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System. Chinese Journal of Electronics,

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.

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., …Tian, Z. (in press). STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation. IEEE Transactions on Computational Social Systems, 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.

Can Federated Models Be Rectified Through Learning Negative Gradients? (2024)
Conference Proceeding
Tahir, A., Tan, Z., & Babaagba, K. O. (2024). Can Federated Models Be Rectified Through Learning Negative Gradients?. In Big Data Technologies and Applications (18-32). https://doi.org/10.1007/978-3-031-52265-9_2

Federated Learning (FL) is a method to train machine learning (ML) models in a decentralised manner, while preserving the privacy of data from multiple clients. However, FL is vulnerable to malicious attacks, such as poisoning attacks, and is challen... Read More about Can Federated Models Be Rectified Through Learning Negative Gradients?.

PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework (2023)
Journal Article
Mckeown, S., Aaby, P., & Steyven, A. (in press). PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework. Forensic Science International: Digital Investigation, 48(Supplement), Article 301680

The automated comparison of visual content is a contemporary solution to scale the detection of illegal media and extremist material, both for detection on individual devices and in the cloud. However, the problem is difficult, and perceptual similar... Read More about PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework.

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., …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.

Scalable Multi-domain Trust Infrastructures for Segmented Networks (2023)
Conference Proceeding
Grierson, S., Ghaleb, B., Buchanan, W. J., Thomson, C., Maglaras, L., & Eckl, C. (in press). Scalable Multi-domain Trust Infrastructures for Segmented Networks.

Within a trust infrastructure, a private key is often used to digitally sign a transaction, which can be verified with an associated public key. Using PKI (Public Key Infrastructure), a trusted entity can produce a digital signature, verifying the au... Read More about Scalable Multi-domain Trust Infrastructures for Segmented Networks.

Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices (2023)
Conference Proceeding
Spalding, A., Tan, Z., & Babaagba, K. O. (in press). Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices. In Proceedings of the 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023)

Data recovery for forensic analysis of both hard drives and solid state media presents its own unique set of challenges. Hard drives face mechanical failures and data fragmentation , but their sequential storage and higher success rates make recovery... Read More about Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices.

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
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.

Self-attention is What You Need to Fool a Speaker Recognition System (2023)
Conference Proceeding
Wang, F., Song, R., Tan, Z., Li, Q., Wang, C., & Yang, Y. (in press). Self-attention is What You Need to Fool a Speaker Recognition System.

Speaker Recognition Systems (SRSs) are becoming increasingly popular in various aspects of life due to advances in technology. However, these systems are vulnerable to cyber threats, particularly adversarial attacks. Traditional adversarial attack me... Read More about Self-attention is What You Need to Fool a Speaker Recognition System.

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)
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.