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PhD Final Exam – Md Amran Siddiqui

Anomaly Detection: Theory, Explanation and User Feedback

Anomaly detection has been used in variety of applications in practice, including cyber-security, frauds detection and detecting faults in safety critical systems etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most anomaly detectors provide no explanations about why an instance was considered anomalous. To address this issue, we propose a feature based explanation approach called sequential feature explanation (SFE) to help the analyst in his investigation. A second problem with the anomaly detection systems is that they usually produce a large number of false positives due to a mismatch between statistical and semantic anomalies. We address this issue by incorporating human feedback i.e. we develop a human-in-the-loop anomaly detection system which can improve its detection rate with the simple form of true/false positive feedback from the analyst. We show empirically the efficacy and the superior performance of the both of our explanation and feedback approaches on significant cyber security applications including red team attack data and real corporate network data along with a large number of benchmark datasets. We also delve into a set of state-of-the-art anomaly detection techniques to understand why they perform so well with a small number of training examples. We unify their working principle into a common framework underlying different pattern spaces and compute their sample complexity with PAC guarantees. On the empirical side, we investigate for the first time, to the best of our knowledge, learning curves for anomaly detection.

Major Advisor: Alan Fern
Committee: Thomas G. Dietterich
Committee: Prasad Tadepalli
Committee: Raviv Raich
GCR: Debashis Mondal

Friday, June 7, 2019 at 10:00am to 12:00pm

Kelley Engineering Center, 1007
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

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