A Regression Based Spectrum-Sensing Data - Falsification Attack Detection Technique in CWSN

Chatterjee, Pinaki Sankar; Roy, Monideepa

doi:10.1109/icit.2015.25

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…Although plenty of the literature and comprehensive research on SSDF attack and defense has been made, most of them will be not specifically suited for CWSNs [23,24]. Basically, the reason is that the framework of resourceconstrained sensor nodes are not be considered, especially in aspect of the limitation of processing capacity and battery power.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Yao

Yuan

et al. 2020

IEEE Access

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Due to the potential applicability of spectrum sensing, cognitive wireless sensor networks have attracted plenty of interest in the research community to improve the bandwidth utilization for practical applications. To alleviate the effect of multi-path fading and resolve the problem of hidden terminal, collaborative spectrum sensing (CSS) is regarded as effective technology to obtain better sensing accuracy. However, CSS is usually vulnerable to the attack behaviors originated from malicious sensor nodes. In this paper, an enhanced cooperative spectrum sensing scheme against SSDF attack based on Dempster-Shafer evidence theory for cognitive wireless sensor networks is introduced. First, the holistic credibility of sensor nodes can be evaluated according to the real-time difference between them and the statistical sensing behaviors. Furthermore, the basic probability assignment function can be defined based on evidence theory, and the credibility of sensor nodes can be estimated. Finally, by using the weighted probability assignment for each cognitive sensor node, the fusion center can reduce the influence of malicious sensor nodes on the final decision and ensure the reliability of reports from cooperative sensor nodes. Simulation results demonstrate that the proposed method can resist SSDF attacks significantly and outperform the traditional secure schemes in aspect of sensing accuracy.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Yao

Yuan

et al. 2020

IEEE Access

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show abstract

“…Our algorithm has a low false‐positive rate and it has lower time and space overheads than the bloom filter‐based algorithm. The running time, memory requirement and communication overheads of our proposed algorithm are explained in Section 7.1, which is less compared to the existing algorithm in the literature. (ii) The maximum match filter (MMF) algorithm is used to detect the SSDF attack [12]. When the CND algorithm is used along with the MMF algorithm, the performance of the MMF algorithm is enhanced.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…Our algorithm will consider a maximum number of similar reports for a collaborative spectrum sensing decision [12]. We make a regression plane depending upon the similarity of different sensing results to avoid false sensing reports and to take proper decisions about the spectrum band.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this paper is to show how node cloning attacks badly impact a collaborative system which is based on the majority voting scheme and how to combat it using the CND algorithm. We have explained the MMF algorithm [12] to prevent the SSDF attack and also proposed the CND algorithm to detect node cloning attack. We have implemented these two algorithms and analysed how the CND algorithm helps the MMF algorithm to produce better results.…”

Section: Introductionmentioning

confidence: 99%

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Lightweight cloned‐node detection algorithm for efficiently handling SSDF attacks and facilitating secure spectrum allocation in CWSNs

Chatterjee

Roy

2018

IET wirel. sens. syst.

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Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilization when compared with normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. While doing so, however, CWSNs are subject to several security threats. The spectrum sensing data falsification attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the resulting collaborative spectrum sensing decision becomes wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of other's network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In the node cloning attack, the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The maximum-match filtering (MMF) algorithm is used for making a secure spectrum sensing decision in CWSNs. The Cloned-Node Detection (CND) algorithm is proposed here to detect cloned nodes. This study also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack.

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“…In this scenario, the original licensed owner of the spectrum is called the Primary User and the cognitive sensor nodes are called the Secondary User [5]. This method of spectrum sharing is called Opportunistic Spectrum Sharing (OSS) [4] [1].The benefit of the OSS technique can be enjoyed by the user only when the network is properly deployed and the security threats of such networks can be robustly handled [14]. To ensure the trustworthiness of the network the spectrum sensing process is an important problem that needs to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Detecting PUE Attack by Measuring Aberrational Node Behavior in CWSN

Chatterjee

Roy

2018

J. Inter. Net.

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Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSN), where malicious secondary users (SU) try to emulate primary users (PU) to maximize their own spectrum usage or obstruct other SU from accessing the spectrum. In this paper, we have designed an application to monitor the SU’s behavior with respect to the CWSN normal behavior profile towards it’s one hop neighbor. Abnormal behavior towards PUE attack of any SU helps us to identify PUE attackers in the network. Our application does not require extensive computational capabilities and memory and therefore suitable for resource constraint cognitive sensor nodes.

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A Regression Based Spectrum-Sensing Data - Falsification Attack Detection Technique in CWSN

Cited by 14 publications

References 11 publications

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Lightweight cloned‐node detection algorithm for efficiently handling SSDF attacks and facilitating secure spectrum allocation in CWSNs

Detecting PUE Attack by Measuring Aberrational Node Behavior in CWSN

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