On Handling Class Imbalance in Continual Learning based Network Intrusion Detection Systems

Amalapuram, Suresh Kumar and Reddy, Thushara Tippi and Channappayya, Sumohana S. and Tamma, Bheemarjuna Reddy (2021) On Handling Class Imbalance in Continual Learning based Network Intrusion Detection Systems. In: 1st International Conference on AI-ML-Systems, AIMLSystems 2021, 21 October 2021 through 23 October 2021, Virtual, Online.

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Modern-day cyber threats are growing more rapidly than ever before. To effectively defend against them, Anomaly-based Network intrusion detection systems (A-NIDS) must evolve continuously. Traditional machine learning techniques are ineffective in handling sequentially evolving tasks, and Neural Networks (NNs) in particular suffer from Catastrophic Forgetting (CF) of old tasks when trained on new ones. Continual Learning (CL) strategies help to mitigate CF by imposing constraints while training NNs on sequentially evolving data like network traffic. However, applying the CL framework in the design of A-NIDS is not straightforward due to the heavy Class Imbalance (CI) in the network traffic datasets. As a result, the performance of the system is very sensitive to the task execution order. In this work, we propose a CL based A-NIDS by applying sample replay with Class Balancing Reservoir Sampling (CBRS) to mitigate CI in a Class Incremental Setting (CIS). Using the CICIDS-2017 dataset, experiments are conducted by permuting the majority class across the different task execution orders using the proposed CL based A-NIDS. We find that using auxiliary memory with context-aware sample replacing strategies, CF can be reduced to a greater extent, as opposed to data augmentation techniques which may alter the original data distribution and increase training time (with oversampling methods). © 2021 ACM.

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IITH Creators:
IITH CreatorsORCiD
Channappayya, Sumohana S.https://orcid.org/0000-0002-5880-4023
Tamma, Bheemarjuna Reddyhttps://orcid.org/0000-0002-4056-7963
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Catastrophic forgetting; Class imbalance.; Continual learning; Deep learning; Network intrusion detection system (NIDS)
Subjects: Computer science
Electrical Engineering
Divisions: Department of Computer Science & Engineering
Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 12 Sep 2022 07:20
Last Modified: 12 Sep 2022 07:20
URI: http://raiith.iith.ac.in/id/eprint/10537
Publisher URL: http://doi.org/10.1145/3486001.3486231
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