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Introducing Deep Learning Self-adaptive Misuse Network Intrusion Detection Systems

INTRODUCING DEEP LEARNING SELF-ADAPTIVE MISUSE NETWORK INTRUSION DETECTION SYSTEMS

  • Project Code :
  • TCREJA19_96
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INTRODUCING DEEP LEARNING SELF-ADAPTIVE MISUSE NETWORK INTRUSION DETECTION SYSTEMS

Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems

Abstract

Intrusion detection systems (IDSs) are essential elements when it comes to the protection of an ICT infrastructure. Misuse IDSs a stable method that can achieve high attack detection rates (ADR), while keeping warning rates under acceptable levels. However, misuse IDSs suffers from the lack of agility, as they are unqualified to adapt to new and “unknown” environments. That is, such IDS put a security administrator into an intensive engineering task for keeping the IDS up-to-date every time it faces efficiency drops. Considering the extended size of modern networks and the complexity of big network traffic data, the problem exceeds by far the limits of human managing capabilities. In this regard, we propose a unique methodology which combines the benefits of self-taught learning and MAPE-K frameworks to deliver a scalable, self-adaptive and autonomous misuse IDS. Our methodology enables a misuse IDS to sustain a high ADR even if it's imposed to consecutive and drastic environmental changes. Through the utilization of deep-learning based methods, the IDS are able to understand an attack’s nature based on generalized features reconstructions stemming directly from the unknown environment and its unlabelled data. The experimental results reveal that our methodology can breathe new life into the IDS without the constant need of manually refreshing its training set. We measure our proposal under several classification metrics, and we show that it is able to increase the ADR of the IDS up to 73.37% in critical situations where a statically trained IDS is rendered totally ineffective.

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