Threat Intelligence using Fusion Hidden Markov Model

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Extending the topic of my final year project of Predictive Threat Intelligence, particularly in developing novel algorithm for understanding attacker behaviour and attack pattern to better predict attackers future actions. Link to my previous work in the domain link

In this work, we propose a framework for inspecting and modelling the behavioural aspect of an attacker to obtain better insight predictive power on his future actions. For modelling we propose a novel semi-supervised algorithm called Fusion Hidden Markov Model (FHMM) which is more robust to noise, requires comparatively less training time, and utilizes the benefits of ensemble learning to better model temporal relationships in data. We further evaluate the performances of FHMM and compares it with both traditional algorithms like Markov Chain, Hidden Markov Model (HMM) and recently developed Deep Recurrent Neural Network (Deep RNN) architectures. We conduct the experiments on dataset consisting of real data attacks on a Cowrie honeypot system. FHMM provides accuracy comparable to deep RNN architectures at significant lower training time. Given these experimental results, we recommend using FHMM for modelling discrete temporal data for significantly faster training and better performance than existing methods.

If the above seems interesting to you, do check out our full paper for theortical proof of the algorithm and the system developed on top of that: link