Unsupervised insider detection through neural feature learning and model optimisation

The insider threat is a significant security concern for both organizations and government sectors. Traditional machine learning-based insider threat detection approaches usually rely on domain focused feature engineering, which is expensive and impractical. In this paper, we propose an autoencoder-based approach aiming to automatically learn the discriminative features of the insider behaviours, thus alleviating security experts from tedious inspection tasks. Specifically, a Word2vec model is trained with a corpus transformed from various security logs to generate event representations. Instead of manually selecting Word2vec model parameters, we develop an autoencoder-based "parameter tuner" for the model to produce an optimal feature set. Then, the detection is undertaken by examining the reconstruction error of an autoencoder for each transformed event using the Carnegie Mellon University (CMU) CERT Programs insider threat database. Experimental results demonstrate that our proposed approach could achieve an extremely low false-positive rate (FPR) with all malicious events identified