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.
Highlights
Wireless sensor networks (WSNs) and their applications have moved to the forefront of research interest due to the tremendous potential in both civil and military environments [1,2]
As an important problem in WSNs, intrusion detection is defined as a WSN system for detecting mobile intruders that are trying to traverse the WSN’s field of interest (FoI) and reach the destination
The two proposed algorithms are integrated into a framework for intrusion detection analysis in WSNs, which enables us to examine the impact of intruder’s intelligence on the intrusion detection capability of a WSN under various circumstances
Summary
Wireless sensor networks (WSNs) and their applications have moved to the forefront of research interest due to the tremendous potential in both civil and military environments [1,2]. Typical application scenarios include battlefield surveillance [3], illegal border crossing [1], malicious vehicle tracking [4], search and rescue [5], civilian vehicle tracking and autonomous interception [6], object tracking [7,8], wild animal tracking [9], etc. As an important problem in WSNs, intrusion detection is defined as a WSN system for detecting mobile intruders that are trying to traverse the WSN’s field of interest (FoI) and reach the destination. The goal of intrusion detection using a WSN is to detect or prevent the intruder(s) from traversing the monitored FoI
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