Biblio

Detection of multi-stage threats such as Advanced Persistent Threats (APT) is extremely challenging due to their deceptive approaches. Sequential events of threats might look benign when performed individually or from different addresses. We propose a new unsupervised framework to identify patterns and correlations of malicious behaviours by analysing heterogeneous log-files. The framework consists of two main phases of data analysis to extract inner-behaviours of log-files and then the patterns of those behaviours over analysed files. To evaluate the framework we have produced a (publicly available) labelled version of the SotM43 dataset. Our results demonstrate that the framework can (i) efficiently cluster inner-behaviours of log-files with high accuracy and (ii) extract patterns of malicious behaviour and correlations between those patterns from real-world data.

As the development of technology increases, the security risk also increases. This has affected most organizations, irrespective of size, as they depend on the increasingly pervasive technology to perform their daily tasks. However, the dependency on technology has introduced diverse security vulnerabilities in organizations which requires a reliable preparedness for probable forensic investigation of the unauthorized incident. Keystroke dynamics is one of the cost-effective methods for collecting potential digital evidence. This paper presents a keystroke pattern analysis technique suitable for the collection of complementary potential digital evidence for forensic readiness. The proposition introduced a technique that relies on the extraction of reliable behavioral signature from user activity. Experimental validation of the proposition demonstrates the effectiveness of proposition using a multi-scheme classifier. The overall goal is to have forensically sound and admissible keystroke evidence that could be presented during the forensic investigation to minimize the costs and time of the investigation.