Collaborative spectrum sensing is vulnerable to data falsification attacks, where malicious secondary users (attackers) submit manipulated sensing reports to mislead the fusion center's decision on spectrum occupancy. This paper considers a challenging attack scenario, where multiple attackers cooperatively maximize their aggregate spectrum utilization. Without attack-prevention mechanisms, we show that honest secondary users (SUs) are unable to opportunistically transmit over the licensed spectrum and may even get penalized for collisions caused by attackers. To prevent such attacks, we propose two attack-prevention mechanisms with direct and indirect punishments. Our key idea is to identify collisions with the primary user (PU) that should not happen if all SUs follow the fusion center's decision. Unlike prior work, the proposed simple mechanisms do not require the fusion center to identify and exclude attackers. The direct punishment can effectively prevent all attackers from behaving maliciously. The indirect punishment is easier to implement and can prevent attacks when the attackers care enough about their long-term reward.
There has been a growing interest in attack-resilient collaborative spectrum sensing in CRNs (e.g., @cite_16 @cite_9 @cite_3 @cite_17 @cite_15 ). Liu @cite_12 exploited the problem of detecting unauthorized usage of a primary licensed spectrum. In this work, the path-loss effect is studied to detect anomalous spectrum usage, and a machine-learning technique is proposed to solve the general case. Chen @cite_9 focused on a passive approach with robust signal processing, and investigated robustness of various data-fusion techniques against sensing-targeted attacks. Kaligineedi @cite_16 presented outlier detection schemes to identify abnormal sensing reports. Min @cite_3 proposed a mechanisms for detecting and filtering out abnormal sensing reports by exploiting shadow-fading correlation in received primary signal strengths among nearby SUs. Fatemieh @cite_15 used outlier measurements inside each SU cell and collaboration among neighboring cells to identify cells with a significant number of malicious nodes. Li in @cite_17 detected possible abnormalities according to SU sensing report histories.
@cite_9 @cite_3 @cite_15 @cite_16 @cite_12 @cite_17
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Distributed spectrum sensing (DSS) enables a Cognitive Radio (CR) network to reliably detect licensed users and avoid causing interference to licensed communications. The data fusion technique is a key component of DSS. We discuss the Byzantine failure problem in the context of data fusion, which may be caused by either malfunctioning sensing terminals or Spectrum Sensing Data Falsification (SSDF) attacks. In either case, incorrect spectrum sensing data will be reported to a data collector which can lead to the distortion of data fusion outputs. We investigate various data fusion techniques, focusing on their robustness against Byzantine failures. In contrast to existing data fusion techniques that use a fixed number of samples, we propose a new technique that uses a variable number of samples. The proposed technique, which we call Weighted Sequential Probability Ratio Test (WSPRT), introduces a reputation-based mechanism to the Sequential Probability Ratio Test (SPRT). We evaluate WSPRT by comparing it with a variety of data fusion techniques under various network operating conditions. Our simulation results indicate that WSPRT is the most robust against the Byzantine failure problem among the data fusion techniques that were considered. Accurate sensing of the spectrum condition is of crucial importance to the mitigation of the spectrum scarcity problem in dynamic spectrum access (DSA) networks. Specifi- cally, distributed sensing has been recognized as a viable means to enhance the incumbent signal detection by exploiting the diversity of sensors. However, it is challenging to make such distributed sensing secure due mainly to the unique features of DSA networks—openness of a low-layer protocol stack in SDR devices and non-existence of communications between primary and secondary devices. To address this challenge, we propose attack-tolerant distributed sensing protocol (ADSP), under which sensors in close proximity are grouped into a cluster, and sensors in a cluster cooperatively safeguard distributed sensing. The heart of ADSP is a novel shadow fading correlation-based filter tailored to anomaly detection, by which the fusion center pre- filters abnormal sensor reports via cross-validation. By realizing this correlation filter, ADSP minimizes the impact of an attack on the performance of distributed sensing, while incurring minimal processing and communications overheads. The efficacy of our scheme is validated on a realistic two-dimensional shadow-fading field, which accurately approximates real-world shadowing envi- ronments. Our extensive simulation-based evaluation shows that ADSP significantly reduces the impact of attacks on incumbent detection performance. Collaborative Sensing is an important enabling tech- nique for realizing opportunistic spectrum access in white space (cognitive radio) networks. We consider the security ramifications of crowdsourcing of spectrum sensing in presence of malicious users that report false measurements. We propose viewing the area of interest as a grid of square cells and using it to identify and disregard false measurements. The proposed mechanism is based on identifying outlier measurements inside each cell, as well as corroboration among neighboring cells in a hierarchical structure to identify cells with significant number of malicious nodes. We provide a framework for taking into considera- tion inherent uncertainties, such as loss due to distance and shadowing, to reduce the likelihood of inaccurate classification of legitimate measurements as outliers. We use simulations to evaluate the effectiveness of the proposed approach against attackers with varying degrees of sophistication. The results show that depending on the attacker-type and location parameters, in the worst case we can nullify the effect of up to 41 of attacker nodes in a particular region. This figure is as high as 100 for a large subset of scenarios. 1 The most important task for a cognitive radio (CR) system is to identify the primary licensed users over a wide range of spectrum. Cooperation among spectrum sensing devices has been shown to offer various benefits including decrease in sensitivity requirements of the individual sensing devices. However, it has been shown in the literature that the performance of cooperative sensing schemes can be severely degraded due to presence of malicious users sending false sensing data. In this paper, we present techniques to identify such malicious users and mitigate their harmful effect on the performance of the cooperative sensing system. Dynamic spectrum access has been proposed as a means to share scarce radio resources, and requires devices to follow protocols that use resources in a proper, disciplined manner. For a cognitive radio network to achieve this goal, spectrum policies and the ability to enforce them are necessary. Detection of an unauthorized (anomalous) usage is one of the critical issues in spectrum etiquette enforcement. In this paper, we present a network structure for dynamic spectrum access and formulate the anomalous usage detection problem using statistical significance testing. The detection problem is classified into two subproblems. For the case where no authorized signal is present, we describe the existing cooperative sensing schemes and investigate the impact of signal path loss on their performance. For the case where an authorized signal is present, we propose three methods that detect anomalous transmissions by making use of the characteristics of radio propagation. Analytical models are formulated for two special cases and, due to the intractability of the general problem, we present an algorithm using machine learning techniques to solve the general case. Our simulation results show that our approaches can effectively detect unauthorized spectrum usage with high detection rate and low false positive rate. Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical cognitive radio systems the data fusion center typically does not know the attacker's strategy. To alleviate the problem of the unknown strategy of attacker(s), an abnormality-detection approach, based on the abnormality detection in data mining, is proposed. The performance of the attacker detection in the single-attacker scenario is analyzed explicitly. For the case in which the attacker does not know the reports of honest secondary users (called independent attack), it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends to infinity. For the case in which the attacker knows all the reports of other secondary users, based on which the attacker sends its report (called dependent attack), an approach for the attacker to perfectly avoid being detected is found, provided that the attacker has perfect information about the miss-detection and false-alarm probabilities. This motivates cognitive radio networks to protect the reports of secondary users. The performance of attacker detection in the general case of multiple attackers is demonstrated using numerical simulations.