A masquerader is an attacker who gains illegitimate access to a user's account. Masquerade detection is one of the key problems of intrusion detection systems. Deep learning models that obtained state-of-the-art results in masquerade detection have failed to exhibit very high detection performance when data samples contain limited information. Alternatively, computationally cheaper and more memoryefficient traditional machine learning models suffer from less robust features, which hinders them in achieving high detection performance. The contributions of this paper are as follows: we introduce new features of variable-length UNIX command sequences (i.e., weighted occurrence frequencies of different orders) and integrate these features into an extended Markov-chain-based variable-length model. The detection performance of our model is evaluated on three publicly available and free datasets: Schonlau (SEA), Purdue (PU), and Greenberg. The results demonstrate that our model significantly improves the true positive rate (TPR), false positive rate, receiver operator characteristic, and threshold variance compared to the baselines (other Markov-chain-based variable-length models). Furthermore, in terms of the TPR, the proposed method is superior to a state-of-the-art deep learning model that uses a convolutional neural network on the PU and Greenberg datasets and a state-of-the-art sequence-alignment-hidden Markov model on the SEA dataset. Moreover, the proposed method is much more lightweight than the state-of-the-art models in terms of computational and memory complexity, and thus more suitable for real-time masquerade detection.
The choice of trustworthy interaction partners is one of the key factors for successful transactions in online communities. To choose the most trustworthy sellers to interact with, buyers rely on trust and reputation models. Therefore, online systems must be able to accurately assess peers’ trustworthiness. The Beta distribution function provides a sound mathematical basis for combining feedback and deriving users’ trustworthiness. But the Beta reputation system suffers from many forms of cheating behavior such as the proliferation of unfair positive ratings, leading a poor service provider to build a good reputation, and the proliferation of unfair negative feedback, leading a good service provider to end up with a bad reputation. In this paper, we propose a new and coherent method for computing users’ trustworthiness by combining the Beta trustworthiness expectation function with the credibility function. This novel combination mechanism mitigates the impact of unfair ratings. In comparison with Bayesian trust model, we quantitatively show that our approach provides significantly more accurate estimation of peers’ trustworthiness through feedback gathered from multiple sources. Furthermore, we propose an extension of Bayesian trustworthiness expectation function by introducing the initial trust propensity to allow assessing individuals’ initial trust.
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