In today's interconnected digital world, targeted attacks have become a serious threat to conventional computer systems and critical infrastructure alike. Many researchers contribute to the fight against network intrusions or malicious software by proposing novel detection systems or analysis methods. However, few of these solutions have a particular focus on Advanced Persistent Threats or similarly sophisticated multi-stage attacks. This turns finding domainappropriate methodologies or developing new approaches into a major research challenge. To overcome these obstacles, we present a structured review of semantics-aware works that have a high potential for contributing to the analysis or detection of targeted attacks. We introduce a detailed literature evaluation schema in addition to a highly granular model for article categorization. Out of 123 identified papers, 60 were found to be relevant in the context of this study. The selected articles are comprehensively reviewed and assessed in accordance to Kitchenham's guidelines for systematic literature reviews. In conclusion, we combine new insights and the status quo of current research into the concept of an ideal systemic approach capable of semantically processing and evaluating information from different observation points.
Behavioural analysis has become an important method of today's malware research. Malicious software is executed inside a controlled environment where its runtime behaviour can be studied. Recently, we proposed the concept of not only observing individual executables but a computer system as a whole. The basic idea is to identify malware by detecting anomalies in the way a system behaves. In this paper we discuss our methodology for empirical malware research and highlight its strengths and limitations. Furthermore, we explain the challenges we faced during our research and describe our lessons learned.
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