TIFS Volume 14 Issue 8

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August, 2019

TIFS Volume 14 Issue 8

Low-level criminals, who do the legwork in a criminal organization, are the most likely to be arrested, whereas the high-level ones tend to avoid attention. But crippling the work of criminal organizations is not possible unless investigators can identify the most influential, high-level members and monitor their communication channels.

Current anomaly detection systems (ADSs) apply statistical and machine learning algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced persistent threat actors. In this paper, we propose an adversarial statistical learning mechanism for anomaly detection, outlier Dirichlet mixture-based ADS (ODM-ADS), which has three new capabilities.

Steganographic schemes are commonly designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML)-based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers.

Automated biometric identification systems are inherently challenged to optimize false (non-)match rates. This can be addressed either by directly improving comparison subsystems, or indirectly by allowing only “good quality” biometric queries to be compared.

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