Detection of Attackers in Cognitive Radio Network Using Optimized Neural Networks

Ajay, V. P.; Nesasudha, M.

doi:10.32604/iasc.2022.024839

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…The results of this work show that the algorithm using the ANN has better results in terms of accuracy and other network variables. This method has an accuracy rate improvement of 32% and a 16% energy savings compared to the existing methods [19].…”

Section: Previous Workmentioning

confidence: 90%

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

Muñoz,

Chica Pedraza,

Cubillos-Sánchez

et al. 2023

Future Internet

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The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU.

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Section: Previous Workmentioning

confidence: 90%

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

Muñoz,

Chica Pedraza,

Cubillos-Sánchez

et al. 2023

Future Internet

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“…In [17], an enhanced ANN-based aggressor detection technique is developed. The act of ANN upgraded by employing Immune plasma optimization (IPO) model that is stimulated by human immune method for a disease of COVID-19.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Almuqren,

Maray,

Alotaibi

et al. 2024

IEEE Access

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Malicious user recognition for spectrum sensing in Cognitive Radio Networks (CRNs) is a serious safety feature to safeguard effective and trustworthy process of these systems. Spectrum sensing permits CRNs to identify and employ accessible spectrum bands. As well as it is available to prospective interference and mischievous actions. To preserve network integrity, recognition of malicious consumers is vital. Deep learning (DL) based malicious consumer classification powers advanced neural network frameworks to recognize and flag possible threats inside a network. By examining numerous amounts of information, DL techniques can distinguish patterns as well as anomalies that are connected with malicious user performance plus system intrusions, scams or irregular action. This technique provides flexibility benefit that permits a network to learn and develop in evolving threats. It also offers an effectual revenue of improving network security in the difficult and active digital landscape. Therefore, this article develops an Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing (ODL-MUDSS) in the CRN. The main intention of ODL-MUDSS model focused on automated identification and classification of MUs in CRN. To accomplish this, the ODL-MUDSS model primarily applies deep belief network (DBN) methodology for automated and accurate detection of MUs. In addition, recognition performance of DBN technique can be enhanced by use of sand cat swarm optimization (SCSO) algorithm and thereby improves the detection results. The performance validation of ODL-MUDSS technique is observed under different processes. The comprehensive outcomes stated enhanced performance of ODL-MUDSS model over other existing models with maximum accuracy of 97.75%, precision of 97.75%, recall of 97.75%, and F-score of 97.75%.

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“…If the main operations executed by the IPA are investigated, it can be easily seen that the IPA differs from the most representative meta-heuristics including ACO, ABC, PSO, BA, FA, FWA, DE, and GA [53]. While the exploitation-dominant operations of the IPA are designed in a semi-adaptive manner and adjust the convergence dynamically without changing the initial values of the control parameters, a quasi-deterministic approach is responsible for managing the important part of exploration, and its promising performance has been validated recently for engineering problems such as channel assignment [54], time series prediction [55], wireless sensor deployment [56], signal noise minimization [57], neural network training [58], and also UAV or UCAV path planning [59]. In this study, the plasma transfer scheme of the IP algorithm was changed with a newly introduced approach called extended treatment, and an extended IP algorithm, or ExtIPA, was designed to solve the path-planning problem.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

Aslan

Oktay

2023

Aerospace

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The increasing usage of unmanned aerial vehicles (UAVs) and their variants carrying complex weapon systems, known as unmanned combat aerial vehicles (UCAVs), has triggered a global revolution in complex military and commercial operations and has attracted researcher attention from different engineering disciplines in order to solve challenging problems regarding these modern vehicles. Path planning is a challenging problem for UAV and UCAV systems that requires the calculation of an optimal solution by considering enemy threats, total flight length, fuel or battery consumption, and some kinematic properties such as turning or climbing angles. In this study, the immune plasma (IP or IPA) algorithm, one of the most recent nature-inspired intelligent optimization methods, was modified by changing the default plasma transfer operations with a newly proposed technique called the extended treatment approach; extended IPA (ExtIPA) was then introduced as a path planner. To analyze the solving capabilities of the ExtIPA, 16 cases from five battlefield scenarios were tested by assigning different values to the algorithm-specific control parameters. The paths calculated with ExtIPA were compared with the paths found by planners on the basis of other intelligent optimization techniques. Comparative studies between ExtIPA and other techniques allowed for stating that the extended treatment approach significantly contributes to both the convergence speed and qualities of the obtained solutions and helps ExtIPA in performing better than its rivals in most cases.

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Detection of Attackers in Cognitive Radio Network Using Optimized Neural Networks

Cited by 8 publications

References 17 publications

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

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