CND-IDS: Continual Novelty Detection for Intrusion Detection Systems

--Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML) has emerged as a promising solution for IDS, offering highly accurate intrusion detection. However, ML-IDS solutions often overlook two critical aspects needed to build reliable systems: continually changing data streams and a lack of attack labels. Streaming network traffic and associated cyber attacks are continually changing, which can degrade the performance of deployed ML models. Labeling attack data, such as zero-day attacks, in real-world intrusion scenarios may not be feasible, making the use of ML solutions that do not rely on attack labels necessary. T o address both these challenges, we propose CND-IDS, a continual novelty detection IDS framework which consists of (i) a learning-based feature extractor that continuously updates new feature representations of the system data, and (ii) a novelty detector that identifies new cyber attacks by leveraging principal component analysis (PCA) reconstruction. Our results on realistic intrusion datasets show that CND-IDS achieves up to 6.1 F-score improvement, and up to 6.5 improved forward transfer over the SOT A unsupervised continual learning algorithm. Our code will be released upon acceptance. I NTRODUCTION In today's digital landscape, cybersecurity is essential for safeguarding sensitive data and maintaining trust in digital systems. With cyber threats becoming more sophisticated, organizations must adopt thorough security measures to protect against breaches and unauthorized access [1].