Enabling security in <scp>MANETs</scp> using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm

Shanmuganathan, C.; Boopalan, K.; Elangovan, G.; Kumar, P. J. Sathish

doi:10.1002/ett.4717

Cited by 4 publications

(2 citation statements)

References 29 publications

(61 reference statements)

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“…The elliptical curve cryptography based sailfish optimization algorithm (ECC‐SFOA) was established by Shanmuganathan et al 15 to ensure secure communication in MANET through the selection of optimal private key. Prasanth et al 16 elaborated a principal component analysis based on GWO with a deterministic convolutional neural network (PCA‐GWO‐DCNN) for classifying intrusions in the MANET by selecting optimal features with higher fitness values.…”

Section: Related Workmentioning

confidence: 99%

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

Selvaraj,

Nanjappan,

Nagalingam

et al. 2024

Trans Emerging Tel Tech

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Mobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

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Section: Related Workmentioning

confidence: 99%

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

Selvaraj,

Nanjappan,

Nagalingam

et al. 2024

Trans Emerging Tel Tech

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“…The snugness of a hub is utilized to depict the trouble of a hub to arrive at different hubs through the organization, and is the backhanded impact of a hub. Characterize it as the proportional of the amount of the separation from this hub to any remaining hubs [15].…”

Section: ░ 4 Deep Learning -Process Estimationmentioning

confidence: 99%

Deep Learning Method of Predicting MANET Lifetime Using Graph Adversarial Network Routing

T A,

Christo,

Vivekanandan

et al. 2023

IJEER

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The prominence of mobile ad-hoc networks (MANETs) is on the rise. Within the domain of machine learning, a specialized subset known as deep learning (DL) employs diverse methodologies, each providing unique interpretations of the data it processes. In existing system the vulnerabilities of MANETs to security threats stem from factors such as node mobility, the potential for MANETs to provide economical solutions to real-world communication challenges, decentralized management, and constrained bandwidth. The efficacy of encryption and authentication methods in safeguarding MANETs encounters limitations. Intelligence will be the future development direction of network adaptive optimization technology in response to the increasingly complex mobile communication network. Data from mobile communication is a crucial part of the future information society. This paper propose adaptive optimization scheme , employs a machine learning algorithm that is capable of realizing the optimal parameter configuration and coordinating various optimization objectives in response to changes in state and environment. The coordination and advancement of social, versatile and area administrations make the customary informal organization easily change to portable correspondence organization. Creation of a system that can learn some rules from data and apply them to subsequent data processing is the research objective. This paper examines the machine learning-based algorithm for big data analysis and effectively addresses the issue of communication network data using graph theory and the experimental result shows higher lifetime prediction accuracy compare to previous system.

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Hybrid heuristic driving training-rider optimization algorithm for QoS-aware multicast communication system in MANET

Sarangi,

Nanda,

Lenka

et al. 2024

Australian Journal of Electrical and Electronics Engineering

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Enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm

Cited by 4 publications

References 29 publications

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

Deep Learning Method of Predicting MANET Lifetime Using Graph Adversarial Network Routing

Hybrid heuristic driving training-rider optimization algorithm for QoS-aware multicast communication system in MANET

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