CND-IDS: Continual Novelty Detection for Intrusion Detection Systems
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910835918503936 |
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| author | Fuhrman, Sean Gungor, Onat Rosing, Tajana |
| author_facet | Fuhrman, Sean Gungor, Onat Rosing, Tajana |
| contents | 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. To 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 6.1x F-score improvement, and up to 6.5x improved forward transfer over the SOTA unsupervised continual learning algorithm. Our code will be released upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14094 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CND-IDS: Continual Novelty Detection for Intrusion Detection Systems Fuhrman, Sean Gungor, Onat Rosing, Tajana Cryptography and Security Machine Learning 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. To 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 6.1x F-score improvement, and up to 6.5x improved forward transfer over the SOTA unsupervised continual learning algorithm. Our code will be released upon acceptance. |
| title | CND-IDS: Continual Novelty Detection for Intrusion Detection Systems |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2502.14094 |