A New Model-Based Method to Detect Radio Jamming Attack to Wireless Networks

Ng, Jonathan; Cai, Ziyuan; Yu, Ming

doi:10.1109/glocomw.2015.7414032

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…These statistic features can be classified into the following two categories: 1) statistical features of upper layer [11]- [14]; 2) statistical features of physical layer [15]- [19]. [11]- [14]: In [11], the effective channel utilization is calculated and used as a statistic to detect jamming attacks. This statistic is essentially the sum of channel access activities and signal strengths.…”

Section: A Related Literaturesmentioning

confidence: 99%

“…Although many methods have been proposed for jamming detection [11]- [19], very few methods were designed considering the jamming attacks for mission-critical mMTC applications, and existing methods are not suitable for UAJ detection. This is mainly because of the following reasons:…”

Section: ) Statistical Features Of Physical Layermentioning

confidence: 99%

“…The maximum invariant statistics [18] and the second-order statistics [19] need to apply a complex multi-antenna mechanism, which is not affordable for low-cost MTDs. In addition, statistical features of upper layer [11]- [14] will cause an intolerable delay.…”

Section: ) Statistical Features Of Physical Layermentioning

confidence: 99%

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Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC Networks

Wang

et al. 2023

IEEE Trans. Commun.

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For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the lowcost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink access jamming (UAJ) can increase the number of dropped/retransmitted packets and restrict or prevent the normal device access. To tackle this vital and challenging problem, we propose a novel UAJ detection method based on the joint space-time sparsity (JSTS). Our key insight is that the JSTS-based feature will be significantly impacted if UAJ happens, since only a small fraction of the devices are active and the traffic pattern for each device is sporadic in the normal state. Unlike the existing detection methods under batch mode (i.e., all sample observations are collected before making a decision), the JSTS-based detection is performed in a sequential manner by processing the received signals one by one, which can detect UAJ as quickly as possible. Moreover, the proposed JSTS-based method does not rely on the prior knowledge of the attackers, since it only cares the abrupt change in the JSTS-based feature on each frame. Numerical results evaluate and confirm the effectiveness of our method.

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