Mr. Ömer Sahin Profile

Ömer Sahin

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Proceedings Article | 21 April 2020 Paper

Machine learning based automatic threat level assessment in fiber-optic Distributed Acoustic Sensing (DAS) intrusion detection system

Omer Sahin, Mehmet Umut Demircin, Murat Gevrekci

Proceedings Volume 11413, 1141322 (2020) https://doi.org/10.1117/12.2558400

KEYWORDS: Machine learning, Acoustics, Fiber optics, Sensing systems, Fiber optics sensors, Computer intrusion detection, Sensors, Data modeling

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Fiber-Optic Distributed Acoustic Sensing (DAS) intrusion detection systems provide effective solutions for the border, critical infrastructure, facility, and pipeline security applications. DAS systems are able to detect and classify acoustic vibrations using standard telecommunication fibers buried under the ground or deployed over a fence. Activities of interest captured by the DAS system may not pose the same level of threat depending on the time and location of the activity. For instance, a ground digging activity during the day time in rural areas close to a pipeline is more likely an agricultural event rather than a suspicious illegal tapping on the pipeline. These everyday events can be misleading if the operator is notified with the same audio-visual alarms for both cases and may create frustration in the operator. Therefore assigning threat levels to the activities is an essential feature of the DAS systems to increase their credibility. In this paper, we propose a threat level assessment method that learns the activity density of the area in an unsupervised manner. Activities are scored using a threat metric and they are assigned levels using a novel dynamic thresholding approach.

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