## 2 Related Works
### SSDF and PUE Attack Detection  
An SSDF attack can also be predicted by exploiting frequency and ordering properties [28]. Malicious nodes in the cognitive radio network send the falsified results to the fusion center. Temporal data collected at the fusion center in the form of the sensing reports form a rich audit data set, which can be analyzed to identify the malicious nodes. In this work, anomaly detection techniques are used, which consist of two schemes. At first, the lightweight intrusion detection system is developed by using a frequency property scheme. Secondly, a hidden Markov chain model (HMM) is designed based on the ordering property of data. Here, HMM often has large unstructured parameters, which induce the false detection of the SSDF attacker node. A collaborative approach is used to mitigate the attacks in the cognitive radio networks [29]. Here, the considered attack is the SSDF attack which sends false sensing reports to the fusion center. The collaborative approach during the spectrum sensing process monitors the behavior of the sensing nodes and identifies the malicious and misbehaving sensing nodes. This method measures the reliability of the nodes using a unique value called a belief level. All the sensing nodes are grouped into a number of clusters in which each cluster has one cluster head. The cluster head analyzes the node's behavior and judges the nodes. During sensing report exchange, there is a high chance for the reports to be affected by the attackers. An Agent-Assisted Defence System (AADS) is presented to jointly detect and mitigate SSDF and PUE attacks [30]. Here, an SSDF attack is detected by comparing the sensing report received from SUs and spectrum agent (SA), which is considered to be trusted. For PUE attack detection, the Received Signal Strength (RSS) based localization method is utilized. Finally, the communication in the network is encrypted by using a Hybrid Advanced Encryption Standard (HAES). Here, attack detection accuracy is poor since it only considers the sensing reports, but the node behavior is not considered. This leads to inaccurate detection since the sensing report may also be affected by the environmental condition, which is not yet considered in this work. An energy-efficient localization mechanism based on channel parameters variance is proposed to mitigate the PUE attack in which primary user signal and others signal is distinguished [31]. The proposed algorithm comprises four steps: initialize, register, update and authentication. In the initialize phase, cognitive users initialize their level ID and sector ID. In the registration phase, users register their respective level ID, sector ID, location and node ID to the cognitive base station. In the update phase, moving primary user locations are updated in the location table. Finally, in the authentication phase spectrum requested node authentication key is verified. If its ID is not in the location table, that particular node is represented as a malicious node. Through the authentication procedure, the PUE attacker is detected and removed from the network. Here, authentication is performed based on the location which can be falsified by the attacker to access the network. A cooperative spectrum sensing performance improvement is achieved in the presence of a PUE attack [32]. Security is an important issue in the cognitive radio network because it affects the performance of sensing in the fusion center. In PUEA, a malicious attacker transmits the emulated primary user signal and defrauds the CR users to prevent them from accessing spectrum holes. PUE has the ability to transmit the signal in both vacant and occupied bands, which induces interference in the primary users. In this process, two important PUE parameters are estimated and then used to obtain the optimal voting rule for minimizing the global error probability. In this approach, distinguishing PUs and attackers is complex due to the poor location estimation process.