Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks

Hyder, Chowdhury Sayeed; Grebur, Brendan; Xiao, Li

doi:10.1007/978-3-642-31909-9_9

Cited by 22 publications

(4 citation statements)

References 9 publications

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“…The drawback is the requirement of a trusted authority capable of emulating a Primary User. Hyder et al [11] present an adaptive reputation-based clustering scheme which uses the partitioning around medoid clustering. Applying the same to a distributed system with less number of inputs available and without consistent reputation information is infeasible.…”

Section: Related Workmentioning

confidence: 99%

Multi-fusion Based Distributed Spectrum Sensing against Data Falsification Attacks and Byzantine Failures in CR-MANET

Pongaliur

Xiao

2014

2014 IEEE 22nd International Symposium on Modelling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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In mobile ad-hoc cognitive radio networks (CR-MANET), accurately identifying primary user spectrum occupancy is an important requirement in successful utilization of the spectrum by the secondary user. This is made difficult by malicious mobile secondary users launching spectrum sensing data falsification (SSDF) attacks, byzantine failures of devices, primary user signal fading, and hidden terminal problems. Another ability of these malicious users is to use their mobility to hide under the changing neighborhood. Existing state of the art techniques either consider a centralized approach in decision making or when considering an ad-hoc network, do not take into account the mobility of the devices which adds unique limitations. We present a light weight multi-fusion based distributed spectrum sensing scheme (MFDSS) in a mobile ad-hoc secondary user network to overcome the aforementioned problems. MFDSS allows each secondary user to collect semi-global information to make decisions in a distributed manner. It uses outlier detection and data fusion to remove incorrect sensing data generated by byzantine failures. Reputation information of the devices is used to suppress a SSDF attack. MFDSS incorporates a reputation propagation and fusion scheme to prevent malicious devices from hiding behind changing topology and to maintain the freshness of the reputation information. Additionally, MFDSS includes an incubation period to discourage devices from changing identity to perform a Sybil attack or to mask a bad reputation. Detailed analysis is presented and the results show significant improvement in correct primary user spectrum occupancy identification. We also show that without the reputation propagation of MFDSS, a malicious device is able to move its position and hide its malicious intentions in the new neighborhood.

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Section: Related Workmentioning

confidence: 99%

Multi-fusion Based Distributed Spectrum Sensing against Data Falsification Attacks and Byzantine Failures in CR-MANET

Pongaliur

Xiao

2014

2014 IEEE 22nd International Symposium on Modelling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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“…The detection rate of the PUs and malicious users in the mobile CRN was improved. Hyder et al introduced a reputation‐based clustering algorithm to counter the various attacking strategies, without requiring a priori knowledge of the attacker distribution and complete identification of the malicious users. The error rate of the proposed clustering algorithm was reduced significantly.…”

Section: Related Workmentioning

confidence: 99%

Beamforming‐based feature extraction and RVM‐based method for attacker node classification in CRN

Senthilkumar

Priya²

2016

Int J Communication

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Cognitive radio is a promising technology for the future wireless spectrum allocation to improve the utilization rate of the licensed bands. However, the cognitive radio network is susceptible to various attacks. Hence, there arises a need to develop a highly efficient security measure against the attacks. This paper presents a beamforming-based feature extraction and relevance vector machine (RVM)-based method for the classification of the attacker nodes in the cognitive radio network. Initially, the allocation of the Rayleigh channel is performed for the communication. The quaternary phase shift keying method is used for modulating the signals. After obtaining the modulated signal, the extraction of the beamforming-based features is performed. The RVM classifier is used for predicting the normal nodes and attacker nodes. If the node is detected as an attacker node, then communication with that node is neglected. Particle swarm optimization is applied for predicting the optimal channel, based on the beamforming feature values. Then, signal communication with the normal nodes is started. Finally, the signal is demodulated. The signal-to-noise ratio and bit-error rate values are computed to evaluate the performance of the proposed approach. The accuracy, sensitivity, and specificity of the RVM classifier method are higher than the support vector machine classifier. The proposed method achieves better performance in terms of throughput, channel sensing/probing rate, and channel access delay.KEY WORDS: beamforming-based feature extraction; bit-error rate (BER); quaternary phase shift keying (QPSK); cognitive radio network (CRN); particle swarm optimization (PSO); relevance vector machine (RVM); signal-to-noise ratio (SNR)

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“…Cooperative sensing improves sensitivity and robustness of spectrum sensing with respect to channel impairments and hidden terminal problem [3], but raises an important security issue by making the cognitive radio network vulnerable to spectrum sensing data falsification (SSDF) attacks [7], [8]. Some malicious users (attackers) may flip their sensing results before they report them to the fusion center with the objective of either blocking transmission initiatives (by making idle channels appear busy to other users) or causing transmission failures (by making busy channels appear idle to other users).…”

Section: Introductionmentioning

confidence: 99%

Securing Cognitive Radio Networks with Dynamic Trust against Spectrum Sensing Data Falsification

Sagduyu

2014

2014 IEEE Military Communications Conference

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The paper addresses the problem of spectrum sensing data falsification (SSDF) attacks in cooperative sensing and develops a dynamic trust management scheme to reliably detect and mitigate such attacks in cognitive radio networks. Secondary users (SUs) sense the spectrum to identify and dynamically access the available primary user (PU) channel. To improve sensing reliability, SUs send their sensing reports (subject to sensing error of individual spectrum sensor, e.g., energy detector) to a fusion center, where sensing reports are combined to identify the channel state (idle or busy). Three types of SUs are considered: (i) normal users reporting their sensing results honestly, (ii) attackers reporting their sensing results inaccurately in form of random onoff attacks, (iii) irrelevant users observing independent spectrum events. The consistency of sensing results is checked at the fusion center with channel outcome of scheduled transmissions (obtained under possible feedback errors) and the probabilistic trust of each user is updated with Bayes rule. Then, the optimal decision rule for cooperative sensing is determined dynamically with new trust assignments. Performance is evaluated separately under Gaussian signal assumption and with wireless transmission data collected with actual radios. Results show that dynamic trust management reliably detects user types and eliminates effects of SSDF attacks. Both the sensing error probability and the throughput are significantly improved compared to the case when all users are by default trusted to be normal users. This performance gain is maintained as the number of attackers or irrelevant users increases in the cognitive radio network.

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Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks

Cited by 22 publications

References 9 publications

Multi-fusion Based Distributed Spectrum Sensing against Data Falsification Attacks and Byzantine Failures in CR-MANET

Multi-fusion Based Distributed Spectrum Sensing against Data Falsification Attacks and Byzantine Failures in CR-MANET

Beamforming‐based feature extraction and RVM‐based method for attacker node classification in CRN

Securing Cognitive Radio Networks with Dynamic Trust against Spectrum Sensing Data Falsification

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