Detection of Intelligent Intruders in Wireless Sensor Networks

Yun Wang, William Chu, Sarah Fields, Colleen Heinemann and Zach Reiter
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Yun Wang: Department of Computer Science and Information Systems, Bradley University, 1501 W Bradley Ave, Peoria, IL 61625, USA
William Chu: Department of Computer Science and Information Systems, Bradley University, 1501 W Bradley Ave, Peoria, IL 61625, USA
Sarah Fields: Department of Computer Science and Information Systems, Bradley University, 1501 W Bradley Ave, Peoria, IL 61625, USA
Colleen Heinemann: Department of Computer Science and Information Systems, Bradley University, 1501 W Bradley Ave, Peoria, IL 61625, USA
Zach Reiter: Department of Computer Science and Information Systems, Bradley University, 1501 W Bradley Ave, Peoria, IL 61625, USA

Future Internet, 2016, vol. 8, issue 1, 1-18

Abstract: Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability ( i.e. , the capability to avoid surrounding sensors). Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1) the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2) network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3) it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances.

Keywords: artificial intelligence; intrusion detection; mobile intruder; performance evaluation; simulation; wireless sensor networks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2016
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