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Cyber threat intelligence is one of the emerging areas of focus ininformation security. Much of the recent work has focused on rule-based methodsand detection of network attacks using Intrusion Detection algorithms. In thispaper we propose a framework for inspecting and modelling the behaviouralaspect of an attacker to obtain better insight predictive power on his futureactions. For modelling we propose a novel semi-supervised algorithm calledFusion Hidden Markov Model (FHMM) which is more robust to noise, requirescomparatively less training time, and utilizes the benefits of ensemblelearning to better model temporal relationships in data. This paper evaluatesthe performances of FHMM and compares it with both traditional algorithms likeMarkov Chain, Hidden Markov Model (HMM) and recently developed Deep RecurrentNeural Network (Deep RNN) architectures. We conduct the experiments on datasetconsisting of real data attacks on a Cowrie honeypot system. FHMM providesaccuracy comparable to deep RNN architectures at significant lower trainingtime. Given these experimental results, we recommend using FHMM for modellingdiscrete temporal data for significantly faster training and better performancethan existing methods.

Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models