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Communication Dans Un Congrès Année : 2020

Active Learning for Intrusion Detection Systems

Quang-Vinh Dang

Résumé

Intrusion Detection Systems (IDSs) play a vital role in the modern cyber-security system. The main task of an IDS is to distinguish between benign and malicious network flows. Hence, the researchers and practitioners usually utilize the power of machine learning techniques by considering an IDS as a binary-classifier. Recent research works demonstrate that an ensemble learning algorithm like xgboost can achieve almost perfect classification in the offline configuration. On the other hand, the performance of a simple and lightweight classification algorithm like Naive Bayes can be improved significantly if we can select a proper sub-training set. In this paper, we discuss the usage of active learning in online configuration to reduce the labeling cost but maintaining the classification performance. We evaluate our approach using the popular real-world datasets and showed that our approach outperformed state-of-the-art results.
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Dates et versions

hal-02443773 , version 1 (17-01-2020)
hal-02443773 , version 2 (11-03-2020)

Identifiants

  • HAL Id : hal-02443773 , version 2

Citer

Quang-Vinh Dang. Active Learning for Intrusion Detection Systems. IEEE Research, Innovation and Vision for the Future, Apr 2020, Ho Chi Minh, Vietnam. ⟨hal-02443773v2⟩
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