Robust and secure spectrum sensing in cognitive radio networks

With wireless devices and applications booming, the problem of inefficient utilization of the precious radio spectrum has arisen. Cognitive radio is a key technology to improve spectrum utilization. A major challenge in cognitive radio networks is spectrum sensing, which detects if a spectrum band is being used by a primary user. Spectrum sensing plays a critical role in cognitive radio networks. However, spectrum sensing is vulnerable to security attacks from malicious users. Detecting malicious users is a crucial problem for cognitive radio networks. First, the channel shadowing and fading result in spatial variability and uncertainty of the PU signal, and hence the sensing reports among geographically separated secondary users are usually distinct. This makes it easy for malicious users to hide the dishonest sensing reports under the natural variation of the sensing reports. Second, due to the open and easy reconfiguration nature of cognitive radio, the cognitive radios are more prone to be compromised and, once compromised, they are prone to more diverse misbehavior. This makes the malicious user detection more difficult than finding faulty or misconfigured users whose effects on the cognitive radio networks are more evident and easy to predict. We propose a decentralized scheme to detect malicious users in cooperative spectrum sensing. The scheme utilizes spatial correlation of received signal strengths among secondary users in close proximity. We also propose to use an alternative mean to make our scheme more robust in malicious user detection. Utilizing alternative mean can filter a portion of outliers (extreme sensing results), thus making the mean more close to the true value of sensing results from benign secondary users, and hence increasing detection accuracy. We have also proposed a neighborhood majority voting approach for the secondary users to decide if a specific user is malicious. Cooperative spectrum sensing is vulnerable to the spectrum sensing data falsification attack. Specifically, a malicious user can send a falsified sensing report to mislead other (benign) secondary users to make an incorrect decision on the PU activity. Therefore, detecting the spectrum sensing data falsification attack or identifying the malicious sensing reports is extremely important for robust cooperative spectrum sensing. This dissertation proposes a distributed density based detection scheme to countermeasure the spectrum sensing data falsification attack. Density based detection scheme can effectively exclude the malicious sensing reports from spectrum sensing data falsification attackers, so that a benign secondary user can effectively detect the PU activity in distributed cooperative spectrum sensing. Moreover, density based detection scheme can also exclude abnormal sensing reports from ill-functioned secondary users. Furthermore, we propose another advanced distributed conjugate prior based detection scheme to defend the spectrum sensing data falsification attack. Conjugate prior based detection can effectively exclude abnormal sensing reports from both spectrum sensing data falsification attackers and ill-functioned secondary users. With this scheme, a benign secondary user can effectively detect the PU activity in distributed cooperative spectrum sensing. On the other hand, denial of service attack is one of the most serious threats to cognitive radio networks. By launching denial of service attack over communication channels, the attacker can severely degrade the network performance. The channel jamming attack is one of denial of service attacks that are simple to launch, and difficult to be countermeasured. The jamming attack is a security threat where the attacker interferes a set of communication channels by injecting a continuous jamming signal or non-continuous short jamming pulses. As a result, the communication channels either cannot be accessed or the signal to noise ratio in these channels is heavily deteriorated. We model the jamming and anti-jamming process as a Markov decision process. With this approach, secondary users are able to avoid the jamming attack launched by external attackers and therefore maximize the payoff function. We first use a policy iteration method to solve the problem. However, this approach is computationally intensive. To decrease the computation complexity, Q-function is used as an alternate method. Furthermore, we propose an algorithm to solve the Q-function. In this dissertation, we propose a malicious user detection scheme, a density based SSDF detection scheme, a conjugate prior based SSDF detection scheme, and an anti-jamming algorithm to achieve robust and secure cooperative spectrum sensing in cognitive radio networks. Performance analysis and simulation results show that our proposed schemes can achieve very good performance in detecting malicious users, excluding abnormal sensing reports, and defending the jamming attack, thus improve spectrum sensing performance in cognitive radio networks.