Spectrum Sensing and Mitigation of Primary User Emulation Attack in Cognitive Radio

The overwhelming growth of wireless communication has led to spectrum shortage issues. In recent days, cognitive radio (CR) has risen as a complete solution for the issue. It is an artificial intelligence-based radio which is capable of finding the free spectrum and utilises it by adapting itself to the environment. Hence, searching of the free spectrum becomes the key task of the cognitive radio termed as spectrum sensing. Some malicious users disrupt the decision-making ability of the cognitive radio. Proper selection of the spectrum scheme and decision-making capability of the cognitive reduces the chance of colliding with the primary user. This chapter discusses the suitable spectrum sensing scheme for low noise environment and a trilayered solution to mitigate the primary user emulation attack (PUEA) in the physical layer of the cognitive radio. The tag is generated in three ways. Sequences were generated using DNA and chaotic algorithm. These sequences are then used as the initial seed value for the generation of gold codes. The output of the generator is considered as the authentication tag. This tag is used to identify the malicious user, thereby PUEA is mitigated. Threat-free environment enables the cognitive radio to come up with a precise decision about the spectrum holes

Efficient Advanced Encryption Standard for Securing Cognitive Radio Networks

During the last decade, the CR (Cognitive Radio) came into view as a major wireless technology to resolve the issue of spectrum secrecy and efficient spectrum utilization. However, due to unlicensed (secondary) users, there are various security threats to the CRN (Cognitive Radio Networks). Some malicious users may access the CRN and mislead the secondary users to vacate the occupied channel, which may stop the communication. In this work, we propose a new cryptographic-based algorithm, CRAES (Cognitive Radio-Advanced Encryption Standard), inspired by the traditional AES to secure the CRN. The data of the primary and secondary users is encrypted at the transmitter and decrypted at the receiver. Unlike the conventional AES, we introduce the data-dependent key-generation and shift-rows process. We also reduce the rounds of AES from 10-6 to improve the computational efficiency without compromising the overall security. The experimental results demonstrate the effectiveness of the proposed CR-AES in terms of better security, reliability, and computational efficiency

Contributions to the security of cognitive radio networks

The increasing emergence of wireless applications along with the static spectrum allocation followed by regulatory bodies has led to a high inefficiency in spectrum usage, and the lack of spectrum for new services. In this context, Cognitive Radio (CR) technology has been proposed as a possible solution to reuse the spectrum being underutilized by licensed services. CRs are intelligent devices capable of sensing the medium and identifying those portions of the spectrum being unused. Based on their current perception of the environment and on that learned from past experiences, they can optimally tune themselves with regard to parameters such as frequency, coding and modulation, among others. Due to such properties, Cognitive Radio Networks (CRNs) can act as secondary users of the spectrum left unused by their legal owners or primary users, under the requirement of not interfering primary communications. The successful deployment of these networks relies on the proper design of mechanisms in order to efficiently detect spectrum holes, adapt to changing environment conditions and manage the available spectrum. Furthermore, the need for addressing security issues is evidenced by two facts. First, as for any other type of wireless network, the air is used as communications medium and can easily be accessed by attackers. On the other hand, the particular attributes of CRNs offer new opportunities to malicious users, ranging from providing wrong information on the radio environment to disrupting the cognitive mechanisms, which could severely undermine the operation of these networks. In this Ph.D thesis we have approached the challenge of securing Cognitive Radio Networks. Because CR technology is still evolving, to achieve this goal involves not only providing countermeasures for existing attacks but also to identify new potential threats and evaluate their impact on CRNs performance. The main contributions of this thesis can be summarized as follows. First, a critical study on the State of the Art in this area is presented. A qualitative analysis of those threats to CRNs already identified in the literature is provided, and the efficacy of existing countermeasures is discussed. Based on this work, a set of guidelines are designed in order to design a detection system for the main threats to CRNs. Besides, a high level description of the components of this system is provided, being it the second contribution of this thesis. The third contribution is the proposal of a new cross-layer attack to the Transmission Control Protocol (TCP) in CRNs. An analytical model of the impact of this attack on the throughput of TCP connections is derived, and a set of countermeasures in order to detect and mitigate the effect of such attack are proposed. One of the main threats to CRNs is the Primary User Emulation (PUE) attack. This attack prevents CRNs from using available portions of the spectrum and can even lead to a Denial of Service (DoS). In the fourth contribution of this the method is proposed in order to deal with such attack. The method relies on a set of time measures provided by the members of the network and allows estimating the position of an emitter. This estimation is then used to determine the legitimacy of a given transmission and detect PUE attacks. Cooperative methods are prone to be disrupted by malicious nodes reporting false data. This problem is addressed, in the context of cooperative location, in the fifth and last contribution of this thesis. A method based on Least Median Squares (LMS) fitting is proposed in order to detect forged measures and make the location process robust to them. The efficiency and accuracy of the proposed methodologies are demonstrated by means of simulation

Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting

This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = 95% for authorized network devices, the collective rogue device detection results for SNR ≥ 12 dB include average burst-by-burst RRR ≈ 94% across all 2520 attack scenarios with individual rogue device attack performance spanning 83.32% \u3c RRR \u3c 99.81%