Securing Cognitive Wireless Sensor Networks: A Survey

Fragkiadakis, Alexandros; Angelakis, Vangelis; Τράγος, Ηλίας

doi:10.1155/2014/393248

Cited by 18 publications

(10 citation statements)

References 85 publications

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“…Thus, a message, sent by this node, circulates in a hidden way in the network until it reaches the target. () However, this mechanism cannot be adopted by all the networks such as the cognitive wireless sensor network due to the low power, memory, and computation time constraints.…”

Section: Crns Attacks and Vulnerabilitiesmentioning

confidence: 99%

Trust management in cognitive radio networks: A survey

2017

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Cognitive radio is proposed to solve the interference and underutilized license problems. With the development of the cognitive functionalities, classical and new security threats become challenging. To make wireless cognitive radio networks more secure and more reliable, several approaches have been proposed in the literature. In recent years, trust and reputation management (TRM) techniques are more and more considered for cognitive radio networks to secure them against the malicious and misbehaving users that try to deprive others from using the white space. The scope of this survey is to give a comprehensive overview about the existing TRM-based mechanisms proposed for the cognitive radio networks. We expose the existing classifications of TRM techniques, then we introduce a new classification that takes into account, more exhaustively, different TRM properties and approaches.Int J Network Mgmt. 2018;28:e1999.wileyonlinelibrary.com/journal/nemThe new classification aspects are the following:1. The network topology: Every network topology may strongly affect the choice of the appropriate TRM mechanism. 2. The detection strategy: At the spectrum detection phase, the SU senses individually or cooperatively the PU activities in the channel. Each detection strategy, used to make the final decision, can be secured by different TRM mechanisms. 3. Trust computation: For each node, the trust value can be computed using intelligent or simple methods (Bayesian network, probability, etc) to distinguish between honest, suspicious, and malicious entities. 4. Spectrum management: To control the spectrum allocation and to manage the channel availability decisions, the CRN may use a centralized, distributed, or clustering spectrum sensing technique. For each spectrum management technique, different TRM approaches are proposed to secure the spectrum detection feature.The remainder of this paper is organized as follows: In section 2, we will review the security attacks and vulnerabilities in CRNs. Then, we will define, in Section 3, the concepts of the TRM. Thus, we will illustrate the existing TRM approaches and classifications. In Section 4, we will present our new classification of TRM approaches in the CRNs. CRNS ATTACKS AND VULNERABILITIESWireless networks are vulnerable to several types of attacks and threats. 2 Indeed, attacks can be classified according to the target layer. Briefly, Table 1 illustrates a list of attacks that may affect all types of wireless networks.Cognitive radio networks are vulnerable to conventional wireless networks attacks 3 but suffers further from specific attacks due to their intrinsic cognitive functionalities. In CRNs, the SUs, competing to exploit the white space, can be honest, selfish, faulty, or malicious. Faulty users may send incorrect sensing outcomes due to defective device, fading location, or shadowing zone. The erroneous spectrum detection affects the final decision and degrades the network performance. On the other hand, the selfish and malicious users aim at monopolizing the f...

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Section: Crns Attacks and Vulnerabilitiesmentioning

confidence: 99%

Trust management in cognitive radio networks: A survey

2017

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“…Sensor nodes are mostly battery powered and energy constrained. In addition, the limited computational capability and memory of sensor nodes, along with its energy-constrained nature, means that there is a high probability of node failure [41,42]. Thus, even though the sensor nodes are mostly stationary, the topology of WSNs can be dynamic due to node failure.…”

Section: Wireless Mesh Network Wireless Mesh Network (Wmn)mentioning

confidence: 99%

Application of reinforcement learning for security enhancement in cognitive radio networks

Ling

Yau

Qadir

et al. 2015

Applied Soft Computing

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Please cite this article in press as: M.H. Ling, et al., Application of reinforcement learning for security enhancement in cognitive radio networks, Appl. Soft Comput. J. (2015), http://dx. a b s t r a c tCognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to sense for and opportunistically operate in underutilized licensed channels, which are owned by the licensed users (or primary users, PUs). Cognitive radio network (CRN) has been regarded as the next-generation wireless network centered on the application of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and dynamically reconfigure its operating parameters, including the sensing and transmission channels, for network performance enhancement. This motivates the use of artificial intelligence to enhance security schemes for CRNs. Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration. In addition these techniques cannot prevent an authenticated node from acting maliciously. In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or near-optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs. RL, which is an artificial intelligence technique, has the ability to learn new attacks and to detect previously learned ones. RL has been perceived as a promising approach to enhance the overall security aspect of CRNs. RL, which has been applied to address the dynamic aspect of security schemes in other wireless networks, such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs. We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge, this is the first survey article that focuses on the use of RL-based techniques for security enhancement in CRNs.

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“…The security is proved and the implemented algorithm resists against a brute force attack. We remind that some new research works focus on network sensor security and use encryption algorithms to improve the security of data and energy consumption [16][17][18]; in this context, we aim to implement our CA cryptosystem as a lightweight security protocol. …”

Section: B Performance Testmentioning

confidence: 99%

A fast cryptosystem using reversible cellular automata

Bouchkaren¹,

Lazaar²

2014

IJACSA

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Abstract-This article defines a new algorithm for a secret key cryptosystem using cellular automata which is a promising approach to cryptography. Our algorithm is based on cellular automata built on a set of reversible rules which have the ability to construct unpredictable secret keys using MARGOLUS neighborhood. To prove the feasibility of the algorithm, we present some tests of encryption, decryption and diffusion; a CPU time comparison with an encryption algorithm by blocks as for instance AES-256 is established. On the other hand, the security of the algorithm is proved and the implemented algorithm resists against a brute force attack.

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Securing Cognitive Wireless Sensor Networks: A Survey

Cited by 18 publications

References 85 publications

Trust management in cognitive radio networks: A survey

Trust management in cognitive radio networks: A survey

Application of reinforcement learning for security enhancement in cognitive radio networks

A fast cryptosystem using reversible cellular automata

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