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In this paper, the problem of detecting adversarial methods on the basis of mimicry against locality-based classifiers has been studied in detail.In particular, the locality-based mimicry by noise generation slightly outperformed the padding-based method, but both approaches strengthened the importance of acquiring a representative set of observations for building a more robust adversarial model.An exhaustive revision of the state-of-the-art has been conducted, from which locality-based mimicry by action pruning and noise generation were presented as effective methods for thwarting conventional machine-learning-based masquerade detection capabilities.
In this paper, the problem of detecting adversarial methods on the basis of mimicry against
locality-based classifiers has been studied in detail. An exhaustive revision of the state-of-the-art
has been conducted, from which locality-based mimicry by action pruning and noise generation
were presented as effective methods for thwarting conventional machine-learning-based masquerade
detection capabilities. The first of them assumed that the attacker might limit its intrusion activities
according to guidelines triggered by adversarial models, and the second considered that adversaries
might conduct padding actions with a similar guidance. These tactics serve to demonstrate in the
WUIL dataset that some of the classical machine learning enablers applied to masquerade detection
operated inaccurately in the face of these threats. The two obfuscation methods have proven effective
with promising results when hindering the detective capabilities of the defensive system expressed
as the accurate detection of both legitimate and attack situations. In particular, the locality-based
mimicry by noise generation slightly outperformed the padding-based method, but both approaches
strengthened the importance of acquiring a representative set of observations for building a more
robust adversarial model. These outcomes encouraged the beginning of the design of strengthening
capabilities against similar adversarial behaviors, which are the focus of our current activities that
extend from this line of research.
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