Threat Analysis of Cyber Security in Wireless Adhoc Networks Using Hybrid Neural Network Model

Demidov, Roman; Zegzhda, Peter D.; Kalinin, Maxim O.

doi:10.3103/s0146411618080084

Cited by 29 publications

(4 citation statements)

References 9 publications

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“…Demidov et al [25] examined the difficulty in identification and mitigation of cyber security risks in ad hoc wireless networks. A neural network has been used to analyze the issues in ad hoc networks more accurately.…”

Section: Related Workmentioning

confidence: 99%

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

Manimegalai

Raj

et al. 2022

Wireless Communications and Mobile Computing

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Wireless networks with a large number of peer nodes are known as mobile ad hoc networks (MANETs). In MANETs, the mobility of nodes causes a number of challenges, including path preservation, battery life, safety, dependability, and unexpected connection characteristics. As a result, the network’s quality of service (QoS) would be compromised (QoS). For the discovery and maintenance of pathways in MANETs, the routing protocol is critical. By implementing the multicast routing protocol, the MANET network’s reliability may be improved significantly. Evaluation of multicast routing for quality of service (QoS) is the primary goal of this study. In multicasting, data packets from one node are transmitted to a set of receiver nodes at a time, simultaneously. Multicasting reduces transmission costs. Cluster head selection is one of the challenges in MANET. This proposed research paper optimal route selection (ORS) provides the cluster head selection and alternate cluster head selection for avoiding the failure of the cluster head, generation of the optimal path between the cluster head and member node based on reliability pair factor and node’s energy, and establishment of the path based on maximum energy and number of hops between the nodes (minimum number of hops). In comparison to existing methods, ORS is more effective in the energy-efficient path between base station and cluster head, and member node is provided by an ORS route. Results show that the proposed ORSMAN has higher throughput, minimum latency, minimum jitter, and maximum packet delivery ratio, when compared to the existing methodologies.

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Section: Related Workmentioning

confidence: 99%

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

Manimegalai

Raj

et al. 2022

Wireless Communications and Mobile Computing

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“…By confining the false detection rate, this method achieves better packet delivery and less delay. Demidov et al [17] discussed the issues in challenges in detection and mitigating cyber security threats in ad-hoc wireless networks. The authors have incorporated a neural network for analyzing the challenges in ad-hoc networks with better approximation.…”

Section: Related Workmentioning

confidence: 99%

A Recurrent Reward Based Learning Technique for Secure Neighbor Selection in Mobile AD-HOC Networks

et al. 2021

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Mobile ad-hoc network is an assortment of distinct attribute-based mobile devices that are autonomous and are cooperative in establishing communication. These nodes exploit wireless links for communication that causes injection of the adversaries in the network. Therefore, detection and mitigation of adversaries and anomalies in the network are mandatory to retain its performance. To strengthen this concept, in this article, a novel secure neighbor selection technique using recurrent reward-based learning is introduced. This proposed technique inherits the benefits of conventional routing and intelligent machine learning paradigm for classifying the states of the nodes based on their communication behavior. Thorough learning of the behavior of the nodes unanimously at all the hop-levels of communication enables establishing secure and consistent routing and transmission paths to the destination. The performance of the proposed technique is estimated using the metrics throughput, packet delivery ratio, and delay and detection ratio. Experimental analysis proves the consistency of the proposed technique by improving throughput, packet delivery ratio, and detection ratio under less delay.

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“…Also, these models are self-learning models that learn from the experiences and patterns available in the data, which makes them highly effective against malware and intrusion. However, this method of deep learning is complex and time-consuming, which limits its use in some of the cybersecurity systems [42]. Nevertheless, with continued research of the experts, it is anticipated that these methods will become more popular in future as the complexity of the training process will reduce with time.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security

Alghamdi¹

2020

Int. J. Interact. Mob. Technol.

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The research aimed to conduct an extensive study of machine learning and deep learning methods in cybersecurity. To accomplish the objectives, the research carried out a qualitative study based on secondary data collection to review the available studies and literature. The research has examined three machine learning methods and three deep learning methods to study the most popular techniques used in cybersecurity. During the research, the working mechanism of each method was studied along with their strengths and weaknesses. Also, a comparative discussion has been made to examine the most effective method for cybersecurity. Limitations in the current literature were also identified, and future direction is also given to target and develop the weak areas of machine learning and deep learning methods.

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Threat Analysis of Cyber Security in Wireless Adhoc Networks Using Hybrid Neural Network Model

Cited by 29 publications

References 9 publications

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

A Recurrent Reward Based Learning Technique for Secure Neighbor Selection in Mobile AD-HOC Networks

Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security

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