Applying High-Performance Bioinformatics Tools for Outlier Detection in Log Data

TL;DR

This paper explores the use of high-performance bioinformatics tools for real-time outlier detection in log data to improve anomaly-based cybersecurity measures against sophisticated threats.

Contribution

It introduces a novel application of bioinformatics tools for scalable, real-time log analysis to enhance anomaly detection in cybersecurity.

Findings

Bioinformatics tools effectively handle large log datasets.

The approach improves detection speed and accuracy.

Scalability is achieved for real-time analysis.

Abstract

Most of today's security solutions, such as security information and event management (SIEM) and signature based IDS, require the operator to evaluate potential attack vectors and update detection signatures and rules in a timely manner. However, today's sophisticated and tailored advanced persistent threats (APT), malware, ransomware and rootkits, can be so complex and diverse, and often use zero day exploits, that a pure signature-based blacklisting approach would not be sufficient to detect them. Therefore, we could observe a major paradigm shift towards anomaly-based detection mechanisms, which try to establish a system behavior baseline -- either based on netflow data or system logging data -- and report any deviations from this baseline. While these approaches look promising, they usually suffer from scalability issues. As the amount of log data generated during IT operations is exponentially growing, high-performance analysis methods are required that can handle this huge amount of data in real-time. In this paper, we demonstrate how high-performance bioinformatics tools can be applied to tackle this issue. We investigate their application to log data for outlier detection to timely reveal anomalous system behavior that points to cyber attacks. Finally, we assess the detection capability and run-time performance of the proposed approach.