Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Brinda, V.; Bhuvaneshwari, M.

doi:10.1007/s11277-021-09025-7

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…The number of sub-puzzles in the given cluster head puzzle is denoted by d, which determines the difficulty level of solving the puzzle. Therefore, the sub-puzzle of puzzle, Pm is denoted as, Pm = Pm [1], Pm [2], Pm [3] We assume that, the server is under attack, if the buffer b is full and hence the requesting client will be given with a puzzle to solve, before initiating the protocol PC. The Client request to initiate PCi, is possible only if the client solves the puzzle and gives solution to puzzle P within the time slot T1, the time until which the puzzle is valid.…”

Section: Construction Of Crack the Puzzle Protocolmentioning

confidence: 99%

“…One of the alarming issues in wireless communication is the lack of spectrum space available to meet the growing needs due to the presence of several smart and advanced wireless technologies. Cognitive Radio Networks (CRN) acts as a bridge between the needs and demands by properly utilizing and allocating the spectrum space [1]. CRN is a self-organizing network in which the allocation and reconfiguration happen dynamically depending on the free space availability and need [2].…”

Section: Introductionmentioning

confidence: 99%

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Honesty Factor (HF) based Security Breach Management Technique in Cognitive Radio Network

Varatharajan

Bhuvaneshwari

2022

Preprint

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Cognitive Radio Networks (CRN) is a self-organizing network that does spectrum sensing and allocation on its own. There were many researches done based on security and the ways to enhance the security. But there must be a security management scheme, to prevent malicious users’ presence in the future. To address this challenge this paper has implemented a novel detection and management scheme that performs both detection and management techniques. Every group in the network has a group head, which contacts the global fusion centre for accessing details about the secondary user. The sub-group head willing to allocate unused primary user (PU) space or to give access when the PU is absent will send a request to all subgroups along with the particular secondary user (SU) id to access the behavioural characteristics. The behavioural characteristics are analysed using Crack the Puzzle (CP) methodology. A new user can enter the cluster only if it was able to break the puzzle. If the node cracks the puzzle, the group head updates the Honesty Factor (HF) to 1. If not, the HF is set to zero and the node is disconnected from the cluster. Therefore, the requesting cluster head allows PU and SU pairing based on the HF factor. It is found from the experimental results that the proposed methodology efficiently manages the security issues, allowing only a valid SU to enter the network.

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Section: Construction Of Crack the Puzzle Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Honesty Factor (HF) based Security Breach Management Technique in Cognitive Radio Network

Varatharajan

Bhuvaneshwari

2022

Preprint

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“…In the intrusion detection system, the automatic response module usually has a higher priority. The problems of the information analysis module, the expansion of the network scale, the gradual accumulation of the amount of data generated in the network, and the requirements for the efficient analysis and processing capability of the intrusion detection system are also increasing [17][18].…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

Malicious Network Attack and Intrusion Based on Decision Tree Algorithm

2020

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With the development of the network, the attack methods have become diverse. Malicious attackers can steal users' personal information by mining information. Therefore, this paper intends to study the role of decision tree algorithm in malicious network attacks and intrusions. The purpose is to improve the detection and defense of network attacks through this algorithm to ensure the information security of users. This paper mainly uses the method of experimental comparison and experimental construction to deeply explore the application of decision tree algorithm and PCA feature extraction in the system. The experimental results show that the intrusion accuracy based on decision tree algorithm can reach more than 90%, and the accuracy of PCA feature extraction can increase by 3%.

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“…The PUEA is one of major challenge, where the authorized network can be accessed by the unauthorized or fake people who intrude between PU and SU which provide major loss of security over the network. The PUEA act can be identified by recognizing the malicious activities in the frequency network [1] [2] [3].…”

Section: Introductionmentioning

confidence: 99%

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

Biradar,

Singh,

Patil

2024

Preprint

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The demand for wireless transmission and existence of the cognitive radio networks (CRN) insist the need for detecting the Primary User Emulsion Attack (PUEA) that authenticates the network as the licensed user for accessing the spectrum in CRN. Such condition creates disruptions in the communications. Hence, in this research, PUEA detection in the CRN is proposed using the deep learning and key sharing approach (KCS2TM model). The key sharing approach enables sharing the secret key between Primary User (PU) and Secondary User (SU) that supports the generation of the communication data, which easily assists in detecting the PUEA for which the ChimSp optimizer enabled Bidirectional Long Short Term Memory (CS2TM) model is proposed. The ChimSp optimizer is designed theoretically and mathematically to train the classifier and ensure the improved detection performances. The model provides increased accuracy, highly reliable, reduce computational cost and time are the supremacy of the model. The performance of the model is analyzed with the existing methods, which shows better accuracy sensitivity and specificity of 85.01%, 87.61% and 82.42% compared to other state of art methods.

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Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Cited by 4 publications

References 28 publications

Honesty Factor (HF) based Security Breach Management Technique in Cognitive Radio Network

Honesty Factor (HF) based Security Breach Management Technique in Cognitive Radio Network

Malicious Network Attack and Intrusion Based on Decision Tree Algorithm

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

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