Unknown, Atypical and Polymorphic Network Intrusion Detection: A Systematic Survey
Ulya Sabeel, Shahram Shah Heydari, Khalil El‐Khatib, Khalid Elgazzar
Abstract
Agile network security is paramount in our modern world which is currently dominated by Internet systems and expanding digital spaces. This rapid digital transformation has created more opportunities for cyberattackers to exploit different vulnerabilities and launch sophisticated and continuously evolving cyberattacks. Increasingly, intrusion detection systems are relying on new methods based on Machine Learning (ML) and Deep Learning (DL) techniques to detect and mitigate such cyberattacks. While such techniques normally can identify known network attack patterns with a reasonable degree of success, their ability to identify complicated atypical, polymorphic, and unknown attacks is shown to be limited. In this paper, we present a comprehensive survey of recent research for detecting unknown, atypical, and polymorphic network attacks using DL techniques. We further highlight and discuss the main challenges in this area and identify the future research directions.