Leveraging Siamese networks for one-shot intrusion detection model
Hindy, Hanan and Tachtatzis, Christos and Atkinson, Robert and Brosset, David and Bures, Miroslav and Andonovic, Ivan and Michie, Craig and Bellekens, Xavier (2022) Leveraging Siamese networks for one-shot intrusion detection model. Journal of Intelligent Information Systems, 60 (2). pp. 407-436. ISSN 1573-7675 (https://doi.org/10.1007/s10844-022-00747-z)
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Abstract
The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems (IDS) has been the subject of significant research. Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to retrain the model for every unseen cyber-attack class. However, retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data. Although anomaly detection systems provide a coarse-grained defence against unseen attacks, these approaches are significantly less accurate and suffer from high false-positive rates. Here, a complementary approach referred to as “One-Shot Learning”, whereby a limited number of examples of a new attack-class is used to identify a new attack-class (out of many) is detailed. The model grants a new cyber-attack classification opportunity for classes that were not seen during training without retraining. A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks. The performance of a pre-trained model to classify new attack-classes based only on one example is evaluated using three mainstream IDS datasets; CICIDS2017, NSL-KDD, and KDD Cup’99. The results confirm the adaptability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representations.
ORCID iDs
Hindy, Hanan, Tachtatzis, Christos ORCID: https://orcid.org/0000-0001-9150-6805, Atkinson, Robert ORCID: https://orcid.org/0000-0002-6206-2229, Brosset, David, Bures, Miroslav, Andonovic, Ivan ORCID: https://orcid.org/0000-0001-9093-5245, Michie, Craig ORCID: https://orcid.org/0000-0001-5132-4572 and Bellekens, Xavier;-
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Item type: Article ID code: 82445 Dates: DateEvent5 November 2022Published9 September 2022AcceptedNotes: Special Issue: AI meets Cybersecurity Subjects: Science > Mathematics > Electronic computers. Computer science
Technology > Electrical engineering. Electronics Nuclear engineeringDepartment: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Measurement Science and Enabling TechnologiesDepositing user: Pure Administrator Date deposited: 22 Sep 2022 14:45 Last modified: 11 Nov 2024 13:37 URI: https://strathprints.strath.ac.uk/id/eprint/82445