Practical Intrusion Detection of Emerging Threats

Mills, Ryan; Marnerides, Angelos K; Broadbent, Matthew; Race, Nicholas

Abstract

The Internet of Things (IoT), in combination with advancements in Big Data, communications and networked systems, offers a positive impact across a range of sectors including health, energy, manufacturing and transport. By virtue of current business models adopted by manufacturers and ICT operators, IoT devices are deployed over various networked infrastructures with minimal security, opening up a range of new attack vectors. Conventional rule-based intrusion detection mechanisms used by network management solutions rely on pre-defined attack signatures and hence are unable to identify new attacks. In parallel, anomaly detection solutions tend to suffer from high false positive rates due to the limited statistical validation of ground truth data, which is used for profiling normal network behaviour. In this work we go beyond current solutions and leverage the coupling of anomaly detection and Cyber Threat Intelligence (CTI) with parallel processing for the profiling and detection of emerging cyber attacks. We demonstrate the design, implementation, and evaluation of Citrus: a novel intrusion detection framework which is adept at tackling emerging threats through the collection and labelling of live attack data by utilising diverse Internet vantage points in order to detect and classify malicious behaviour using graph-based metrics as well as a range of machine learning (ML) algorithms. Citrus considers the importance of ground truth data validation and its flexible software architecture enables both the real-time and offline profiling, detection and classification of emerging cyber-attacks under optimal computational costs. Thus, establishing it as a viable and practical solution for next generation network defence and resilience strategies.

• Journal Article Type: Article
• Online Publication Date: Jun 22, 2021
• Publication Date: Mar 1, 2022
• Deposit Date: Feb 16, 2022
• Journal: IEEE Transactions on Network and Service Management
• Print ISSN: 1932-4537
• Electronic ISSN: 2373-7379
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 1
• Pages: 582-600
• DOI: https://doi.org/10.1109/TNSM.2021.3091517
• Keywords: Intrusion Detection, Machine Learning, Cyber Threat Intelligence
• Public URL: http://researchrepository.napier.ac.uk/Output/2844204