4 research outputs found
Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing
Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk
(SW) tests, are proposed to detect and suppress spectrum sensing data
falsification (SSDF) attacks by malicious users in cooperative spectrum
sensing. First, we consider basic and statistical SSDF attacks, where the
malicious users attack independently. Then we propose a new SSDF attack, which
involves cooperation among malicious users by masking. In practice, the number
of malicious users is unknown. Thus, it is necessary to estimate the number of
malicious users, which is found using clustering and largest gap method.
However, we show using Monte Carlo simulations that, these methods fail to
estimate the exact number of malicious users when they cooperate. To overcome
this, we propose a modified largest gap method.Comment: Accepted in Proceedings of 79th IEEE Vehicular Technology
Conference-Spring (VTC-Spring), May 2014, Seoul, South Kore
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
Robust collaborative spectrum sensing in the presence of deleterious users
Collaborative spectrum sensing has attracted significant research attention in the last few years and is widely accepted as a viable approach to improve spectrum sensing reliability. Fusing data from multiple opportunistic users (OUs) in order to produce reliable sensing results implies a reliance on the OU to provide correct information. In the presence of malfunctioning or selfish users, performance of collaborative spectrum sensing deteriorates significantly. In this study, the authors propose mechanisms for the detection and suppression of such deleterious OUs (DOUs) for hard and soft decision fusion. More specifically, a credibility-based mechanism for hard decision fusion using a hard decision combining beta reputation (HDC-BR) system is introduced. The authors proposed method does not require knowledge of the total number of deleterious users in advance. In HDC-BR, the fusion centre assigns and updates weights to each user’s decisions based on an individual user credibility score, which is calculated using the BR system. The presence of DOUs in soft decision-based collaborative spectrum sensing has even more adverse effects on system performance. The authors also propose a scheme for the case of soft decision fusion to detect and eliminate falsified user observations at the fusion centre using a modified Grubbs test; they refer to it as soft-decision combining-modified Grubbs (SDC-MG). They compare the performance of the proposed methods with malicious user detection schemes proposed in the literature as well as with the case where no DOU suppression scheme is implemented, and conclude that SDC-MG performs much better than HDC-BR in a low signal-tonoise ratio regime
Robust Collaborative Spectrum Sensing in the Presence of Deleterious Users
Collaborative spectrum sensing has attracted significant research attention in the last few years and is widely accepted as a viable approach to improve spectrum sensing reliability. Fusing data from multiple Opportunistic users (OUs) in order to produce reliable sensing results implies a reliance on the OU to provide correct information. In the presence of malfunctioning or selfish users, performance of collaborative spectrum sensing deteriorates significantly. In this article, we propose mechanisms for the detection and suppression of such deleterious opportunistic users (DOUs) for hard and soft decision fusion. More specifically, a credibility based mechanism for hard decision fusion using a beta reputation system (HDC-BR) is introduced. Our proposed method does not require knowledge of the total number of deleterious users in advance. In HDC-BR, the fusion center assigns and updates weights to each user’s decisions based on an individual user credibility score which is calculated using the beta reputation system. The presence of DOUs in soft decision based collaborative spectrum sensing has even more adverse effects on system performance. We also propose a scheme for the case of soft decision fusion to detect and eliminate falsified user observations at the fusion centre using a Modified Grubbs Test; we refer to it as SDC-MG. We compare the performance of the proposed methods with malicious user detection schemes proposed in the literature as well as with the case where no DOU suppression scheme is implemented, and conclude that SDC-MG performs much better than HDC-BR in a low Signal to Noise Ratio (SNR) regime