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Advanced Persistent Threats (APTs) are a main impendence in cyber security ofcomputer networks. In 2015, a successful breach remains undetected 146 days onaverage, reported by [Fi16].With our work we demonstrate a feasible and fastway to analyse real world log data to detect breaches or breach attempts. Byadapting well-known kill chain mechanisms and a combine of a time seriesdatabase and an abstracted graph approach, it is possible to create flexibleattack profiles. Using this approach, it can be demonstrated that the graphanalysis successfully detects simulated attacks by analysing the log data of asimulated computer network. Considering another source for log data, theframework is capable to deliver sufficient performance for analysing real-worlddata in short time. By using the computing power of the graph database it ispossible to identify the attacker and furthermore it is feasible to detectother affected system components. We believe to significantly reduce thedetection time of breaches with this approach and react fast to new attackvectors.

Anomaly Detection in Log Data using Graph Databases and Machine Learning to Defend Advanced Persistent Threats