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

Reinforcement learning-based trust and reputation model for spectrum leasing in cognitive radio networks

Cognitive Radio (CR), which is the next generation wireless communication system, enables unlicensed users or Secondary Users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or Primary Users(PUs) so that bandwidth availability improves at the SUs, which helps to improve the overall spectrum utilization. Collaboration, which has been adopted in various schemes such distributed channel sensing and channel access, is an intrinsic characteristic of CR to improve network performance. However, the requirement to collaborate has inevitably open doors to various forms of attacks by malicious SUs, and this can be addressed using Trust and Reputation Management (TRM). Generally speaking, TRM detects malicious SUs including honest SUs that turn malicious. To achieve a more efficient detection, we advocate the use of Reinforcement Learning (RL), which is known to be flexible and adaptable to the changes in operating environment in order to achieve optimal network performance. Its ability to learn and re-learn throughout the duration of its existence provides intelligence to the proposed TRM model, and so the focus on RL-based TRM model in this paper. Our preliminary results show that the detection performance of RLbased TRM model has an improvement of 15% over the traditional TRM in a centralized cognitive radio network. The investigation in the paper serves as an important foundation for future work in this research field

Novel Approaches for the Performance Enhancement of Cognitive Radio Networks

This research is dedicated to the study of the challenges faced by Cognitive Radio (CR) networks, which include self-coexistence of the networks in the spectral environment, security and performance threats from malicious entities, and fairness in spectrum contention and utilization. We propose novel channel acquisition schemes that allow decentralized CR networks to have multiple channel access with minimal spectrum contentions. The multiple channel acquisition schemes facilitate fast spectrum access especially in cases where networks cannot communicate with each other. These schemes enable CR networks to self-organize and adapt to the dynamically changing spectral environment. We also present a self-coexistence mechanism that allows CR networks to coexist via the implementation of a risk-motivated channel selection based deference structure (DS). By forming DS coalitions, CR networks are able to have better access to preferred channels and can defer transmission to one another, thereby mitigating spectrum conflicts. CR networks are also known to be susceptible to Sybil threats from smart malicious radios with either monopolistic or disruptive intentions. We formulate novel threat and defense mechanisms to combat Sybil threats and minimize their impact on the performance of CR networks. A dynamic reputation system is proposed that considerably minimizes the effectiveness of intelligent Sybil attacks and improves the accuracy of spectrum-based decision-making processes. Finally, we present a distributed and cheat-proof spectrum contention protocol as an enhancement of the adaptive On-Demand Spectrum Contention (ODSC) protocol. The Modified On-Demand Spectrum Contention (MODSC) protocol enhances fairness and efficiency of spectrum access. We also show that there is substantial improvement in spectrum utilization with the incorporation of channel reuse into the MODSC protocol

A Hierarchical Structure towards Securing Data Transmission in Cognitive Radio Networks

Cognitive Radio (CR) technology is considered as a promising technology to overcome spectrum scarcity problem in wireless networks, by sharing the spectrum between both unlicensed users (secondary users, (SUs)) and licensed users (primary users, (PUs)), provided that the SUs respect the PUs’ rights to use the spectrum exclusively. An important technical area in cognitive radio networks (CRNs) is wireless security. A secure CRN must meet different security requirements, which are: confidentiality, integrity, availability and authentication. Data confidentiality is a mandatory requirement in cognitive radio networks, generally to maintain the privacy of the data owner (PU or SU). Integrity means that data is transmitted from the source to the destination without alteration. While availability is to release the channels assigned to one SU as soon as a PU wants to use its spectrum. Authentication in CRN means that each node has to authenticate itself before it can use the available spectrum channels. New classes of security threats and challenges in CRNs have been introduced that target the different layers of OSI model and affect the security requirements. Providing strong security may prove to be the most difficult aspect of making CR a long-term commercially-viable concept. Protection of routes used for data transmission is a critical prerequisite to ensure the robustness of iv the routing process. Therefore, route discovery must be done in such a way that lets each node find the best secure path(s) for its data transmission. In this work, network security of CRN is improved through proposing different models that are built to fulfil the security requirements mentioned above. Improving the network security enhances the network performance, taking into consideration the quality of service (QoS) desired by the different network nodes such as bandwidth and time delay. This work aims to combine the spectrum sensing phase and the spectrum management phase, as well as to detect all the adversary nodes that slow down the network performance by selectively holding and not forwarding packets to their next hop(s). We measure the network node’s reliability for using network resources through a value called belief level (BL), which is considered as the main parameter for our entire work. BL is used to monitor the nodes’ behavior during the spectrum sensing phase, and then it is used to form the best path(s) during the spectrum management phase. Particularly, this work follows a hierarchical structure that has three different layers. At the bottom layer, a novel authentication mechanism is developed to fulfil the authentication and the availability security requirements, which ends assigning a belief level (BL) to each node. At the middle layer, the nodes’ behavior during the spectrum sensing phase is monitored to detect all the adversary node(s). Finally, at the top layer, a novel routing algorithm is proposed that uses the nodes’ security (BL) as a routing metric. SUs collaborate with each other to monitor other nodes’ behavior. Users’ data confidentiality and integrity are satisfied through this hierarchical structure that uses the cluster-based, central authority, and nodes collaboration concepts. By doing so, the traffic carried in the CRN is secured and adversary nodes are detected and penalized