Act Proactively: An Intrusion Prediction Approach for Cyber Security

Abstract

Despite the multitude of approaches proposed for intrusion detection, cyberattacks are still a timeless issue for the research community and industry as they cause various devastating effects to companies and organisations. There are limited intrusion prediction approaches in the literature, as the main bulk of methods focuses on cyberattack detection rather than prediction, which would allow the defenders (attack’s targets) to restrain/stop the attack. This work aims to identify known DoS and Probe attack patterns at their very beginning. Specifically, we use machine learning algorithms to predict the malicious packets of DoS and Probe attacks, raising the defender’s awareness to act proactively and stop the attack. To the best of our knowledge, this is the first time that time series analysis and machine learning techniques are used to model the intrusion prediction problem effectively. An extensive experimental study confirms the efficacy of the proposed approach according to multiple evaluation measures.