Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments

Authors: Tara Salman, Deval Bhamare, Aiman Erbad, Raj Jain, Mohammed Samaka
Published: 2018-10-23

Source: https://arxiv.org/abs/1812.05443

PDF: https://arxiv.org/pdf/1812.05443

AIにより推定されたラベル

攻撃検出 マルチクラス分類 情報セキュリティ

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary for appropriate countermeasures and defense against attacks. In this paper, we investigate both detecting and categorizing anomalies rather than just detecting, which is a common trend in the contemporary research works. We have used a popular publicly available dataset to build and test learning models for both detection and categorization of different attacks. To be precise, we have used two supervised machine learning techniques, namely linear regression (LR) and random forest (RF). We show that even if detection is perfect, categorization can be less accurate due to similarities between attacks. Our results demonstrate more than 99 categorization accuracy of 93.6 attacks. Further, we argue that such categorization can be applied to multi-cloud environments using the same machine learning techniques.