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

Selfish Attack Detection in Cognitive Ad-Hoc Network

The wireless technologies have penetrated everyone’s life in various ways in the recent past. So due to increase in the demand for the bandwidth in spectrum, as all the bandwidth allocation done is in static manner so there is scarcity of bandwidth in spectrum. So no bandwidth is left to allocate for new technology Cognitive Radio (CR) is a promising technology that can alleviate the spectrum shortage problem by enabling unlicensed users equipped with CRs to coexist with existing users in licensed spectrum bands while causing no interference to existing communications. Spectrum sensing is one of the essential mechanisms of CRs and its operational aspects are being investigated actively. However, little research has been done regarding security in cognitive radio, while much more research has been done on spectrum sensing and allocation problems. A selfish cognitive radio node can occupy all or part of the resources of multiple channels, prohibiting other cognitive radio nodes from accessing these resources. Selfish cognitive radio attacks are a serious security problem because they significantly degrade the performance of a cognitive radio networ

Robust Resource Allocation for OFDM-based Cognitive Radio in the Presence of Primary User Emulation Attack

Cognitive radio (CR) is a promising solution to improve the spectrum efficiency in which some unlicensed users are allowed to exploit frequency bands which are not used by licensed network. However, CR technology imposes some threats to the network. One of these threats is primary user emulation attack where some malicious users try to send fake signals similar to the primary user (PU) and prevent secondary users from accessing vacant bands. Moreover, the presence of a primary user emulation attacker (PUEA) leads to additional interference to the CR and consequently, the efficiency of conventional power loading algorithms will be degraded. In this paper, we propose a power allocation scheme in an orthogonal frequency-division multiplexing (OFDM) based CR in the presence of PUEA. Power allocation is performed with the aim of maximizing the downlink transmission capacity achieved by the cognitive user, while keeping the interference level at the PU below a predefined threshold. Simulation results confirm the efficiency of our proposed power loading scheme, compared to classical loading algorithms that do not consider the activity of malicious users in the radio environment

Transfer Learning for Device Fingerprinting with Application to Cognitive Radio Networks

Primary user emulation (PUE) attacks are an emerging threat to cognitive radio (CR) networks in which malicious users imitate the primary users (PUs) signals to limit the access of secondary users (SUs). Ascertaining the identity of the devices is a key technical challenge that must be overcome to thwart the threat of PUE attacks. Typically, detection of PUE attacks is done by inspecting the signals coming from all the devices in the system, and then using these signals to form unique fingerprints for each device. Current detection and fingerprinting approaches require certain conditions to hold in order to effectively detect attackers. Such conditions include the need for a sufficient amount of fingerprint data for users or the existence of both the attacker and the victim PU within the same time frame. These conditions are necessary because current methods lack the ability to learn the behavior of both SUs and PUs with time. In this paper, a novel transfer learning (TL) approach is proposed, in which abstract knowledge about PUs and SUs is transferred from past time frames to improve the detection process at future time frames. The proposed approach extracts a high level representation for the environment at every time frame. This high level information is accumulated to form an abstract knowledge database. The CR system then utilizes this database to accurately detect PUE attacks even if an insufficient amount of fingerprint data is available at the current time frame. The dynamic structure of the proposed approach uses the final detection decisions to update the abstract knowledge database for future runs. Simulation results show that the proposed method can improve the performance with an average of 3.5% for only 10% relevant information between the past knowledge and the current environment signals.Comment: 6 pages, 3 figures, in Proceedings of IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Hong Kong, P.R. China, Aug. 201

Prevention of Emulation Attack in Cognitive Radio Networks Using Integrated Authentication

Security is the prominent problem in emerging cognitive radio. Protecting the chief user’s and sub-ordinate user’s right to use the spectrum results in the correct cognitive radio operation. The major user emulation attack is a physical layer attack which disrupts the secondary user’s operation. An Advanced Encryption Standard scheme is used in this work that aims to defeat the chief User Emulation Attack by the correct detection of the chief user. The reference signal is encrypted and transmitted along with the Digital TV signal. Using a shared secret the receiver regenerates the reference and the cross association and the auto correlation are calculated which helps in the accurate detection of the chief user as well as the malicious user. The simulations were carried out and the results show that the detection scheme results in zero misdetection and also false alarm which is below a set threshold

A Survey on the Communication Protocols and Security in Cognitive Radio Networks

A cognitive radio (CR) is a radio that can change its transmission parameters based on the perceived availability of the spectrum bands in its operating environment. CRs support dynamic spectrum access and can facilitate a secondary unlicensed user to efficiently utilize the available underutilized spectrum allocated to the primary licensed users. A cognitive radio network (CRN) is composed of both the secondary users with CR-enabled radios and the primary users whose radios need not be CR-enabled. Most of the active research conducted in the area of CRNs has been so far focused on spectrum sensing, allocation and sharing. There is no comprehensive review paper available on the strategies for medium access control (MAC), routing and transport layer protocols, and the appropriate representative solutions for CRNs. In this paper, we provide an exhaustive analysis of the various techniques/mechanisms that have been proposed in the literature for communication protocols (at the MAC, routing and transport layers), in the context of a CRN, as well as discuss in detail several security attacks that could be launched on CRNs and the countermeasure solutions that have been proposed to avoid or mitigate them. This paper would serve as a good comprehensive review and analysis of the strategies for MAC, routing and transport protocols and security issues for CRNs as well as would lay a strong foundation for someone to further delve onto any particular aspect in greater depth

Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques

Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely, the relay and replay attacks. Such threats are conducted by malicious nodes that replicate the transmissions of a real primary user (PU), thus making them resilient to many defensive procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength (RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are presented in the paper: parametric and nonparametric. The parametric scheme is based on the likelihood ratio test (LRT) and requires the existence of a precise path loss model for the observed RSS values. On the contrary, the nonparametric procedure is not tied to any particular propagation model; so, it does not require any calibration process and is robust to changing environmental conditions. Simulations show that the nonparametric detection approach is comparable in performance to the LRT under moderate shadowing conditions, specially in case of replay attacks