All Outputs (2)

Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification (2022)
Journal Article
McCarthy, A., Ghadafi, E., Andriotis, P., & Legg, P. (2023). Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification. Journal of Information Security and Applications, 72, Article 103398. https://doi.org/10.1016/j.jisa.2022.103398

Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks. Recently, there has been growing interest in the domain of adversarial mach... Read More about Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification.

Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey (2022)
Journal Article
McCarthy, A., Ghadafi, E., Andriotis, P., & Legg, P. (2022). Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey. Journal of Cybersecurity and Privacy, 2(1), 154-190. https://doi.org/10.3390/jcp2010010

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introdu... Read More about Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey.