Mining intrusion detection alert logs to minimise false positives & gain attack insight

Utilising Intrusion Detection System (IDS) logs in security event analysis is crucial in the process of assessing, measuring and understanding the security state of a computer network, often defined by its current exposure and resilience to network attacks. Thus, the study of understanding network attacks through event analysis is a fast growing emerging area. In comparison to its first appearance a decade ago, the complexities involved in achieving effective security event analysis have significantly increased. With such increased complexities, advances in security event analytical techniques are required in order to maintain timely mitigation and prediction of network attacks. This thesis focusses on improving the quality of analysing network event logs, particularly intrusion detection logs by exploring alternative analytical methods which overcome some of the complexities involved in security event analysis. This thesis provides four key contributions. Firstly, we explore how the quality of intrusion alert logs can be improved by eliminating the large volume of false positive alerts contained in intrusion detection logs. We investigate probabilistic alert correlation, an alternative to traditional rule based correlation approaches. We hypothesise that probabilistic alert correlation aids in discovering and learning the evolving dependencies between alerts, further revealing attack structures and information which can be vital in eliminating false positives. Our findings showed that the results support our defined hypothesis, aligning consistently with existing literature. In addition, evaluating the model using recent attack datasets (in comparison to outdated datasets used in many research studies) allowed the discovery of a new set of issues relevant to modern security event log analysis which have only been introduced and addressed in few research studies. Secondly, we propose a set of novel prioritisation metrics for the filtering of false positive intrusion alerts using knowledge gained during alert correlation. A combination of heuristic, temporal and anomaly detection measures are used to define metrics which capture characteristics identifiable in common attacks including denial-of-service attacks and worm propagations. The most relevant of the novel metrics, Outmet is based on the well known Local Outlier Factor algorithm. Our findings showed that with a slight trade-off of sensitivity (i.e. true positives performance), outmet reduces false positives significantly. In comparison to prior state-of-the-art, our findings show that it performs more efficiently given a variation of attack scenarios. Thirdly, we extend a well known real-time clustering algorithm, CluStream in order to support the categorisation of attack patterns represented as graph like structures. Our motive behind attack pattern categorisation is to provide automated methods for capturing consistent behavioural patterns across a given class of attacks. To our knowledge, this is a novel approach to intrusion alert analysis. The extension of CluStream resulted is a novel light weight real-time clustering algorithm for graph structures. Our findings are new and complement existing literature. We discovered that in certain case studies, repetitive attack behaviour could be mined. Such a discovery could facilitate the prediction of future attacks. Finally, we acknowledge that due to the intelligence and stealth involved in modern network attacks, automated analytical approaches alone may not suffice in making sense of intrusion detection logs. Thus, we explore visualisation and interactive methods for effective visual analysis which if combined with the automated approaches proposed, would improve the overall results of the analysis. The result of this is a visual analytic framework, integrated and tested in a commercial Cyber Security Event Analysis Software System distributed by British Telecom.