Ali Elghariani

Yahya Javed, Mosab A. Khayat, Ali A. Elghariani et al.

The increase in scale of cyber networks and the rise in sophistication of cyber-attacks have introduced several challenges in intrusion detection. The primary challenge is the requirement to detect complex multi-stage attacks in realtime by processing the immense amount of traffic produced by present-day networks. In this paper we present PRISM, a hierarchical intrusion detection architecture that uses a novel attacker behavior model-based sampling technique to minimize the realtime traffic processing overhead. PRISM has a unique multi-layered architecture that monitors network traffic distributedly to provide efficiency in processing and modularity in design. PRISM employs a Hidden Markov Model-based prediction mechanism to identify multi-stage attacks and ascertain the attack progression for a proactive response. Furthermore, PRISM introduces a stream management procedure that rectifies the issue of alert reordering when collected from distributed alert reporting systems. To evaluate the performance of PRISM, multiple metrics have been proposed, and various experiments have been conducted on a multi-stage attack dataset. The results exhibit up to 7.5x improvement in processing overhead as compared to a standard centralized IDS without the loss of prediction accuracy while demonstrating the ability to predict different attack stages promptly.

Tawfeeq Shawly, Ali Elghariani, Jason Kobes et al.

With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.