An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

Rajeshkumar, G.; Kumar, Mithilesh; Kumar, Kamlesh; Bhatia, Surbhi; Mashat, Arwa; Dadheech, Pankaj

doi:10.32604/csse.2023.026137

Cited by 21 publications

(6 citation statements)

References 18 publications

(14 reference statements)

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“…Pandey and Singh [16] did black hole detection using a machine learning algorithm. Rajeshkumar et al [17] used cluster trust adaptive ack, Kalman filtering technique, and swarm optimization to identify black hole attacker, outcome of this research provides 3.3% improvement in PDR and 3.5% improvement in male ware detection when compared with CTAAPSO methods. Black hole detection algorithm proposed in the study [5] using DHMD 5 and compute the performance metric which yields 23% and reduce the memory overhead.…”

Section: Introductionmentioning

confidence: 83%

Enhancing MANET Security: A Watch Dog Routing Algorithm Approach for Intruder and Black Hole Attack Detection

Hemalatha,

Vijayakumar,

Gurunathan

et al. 2024

IJCMEM

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When wireless nodes communicate without the use of infrastructure, the network is subject to security breaches. Mobile Adhoc Network (MANET) is one of the most vulnerable wireless networks in terms of security breaches. The most common types of security breaches are intruders and attackers, whose tasks are to reduce the internal performance of the network. Many research studies are focused on detecting and preventing these two security threads. This article focuses on intruder and black hole attackers and their communication. Several techniques were proposed for thwart the intruders and attackers in the Mobile Adhoc Network communication by using the modern technologies which are an additional load to the nodes operation and these techniques could not be able to predict the attacker before it was done. To achieve this goal, this article proposed the Watch Dog approach involves routing protocol to monitoring the forwarding time of all nodes in the transmission. Delays in forwarded time nodes could indicate an intruder, while discarding the forwarded node could indicate a black hole attacker. The proposed Watch Dog routing algorithm with classification technique was implemented with a network simulator with Adhoc On Demand Vector protocol named as WD-AODV, and the simulation results were compared to a modern techniques of Fuzzy Logic based AODV (FL-AODV), Machine Learning-based AODV (ML-AODV) and Artificial Intelligence based AODV (AI-AODV) routing protocol. The compared results of attack rates, attack detection time, Packet delivery ratio and End to End delay showed that the Watch Dog-based attacker and intruder detection methods perform better by more than 59%, with excellent performance factors of 69%.

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Section: Introductionmentioning

confidence: 83%

Enhancing MANET Security: A Watch Dog Routing Algorithm Approach for Intruder and Black Hole Attack Detection

Hemalatha,

Vijayakumar,

Gurunathan

et al. 2024

IJCMEM

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show abstract

“…The proposed CBGWO algorithm is compared with other existing methods such as PCA-FELM, 23 CTAA-MPSO, 27 FLS-RE, 29 LEACH, 33 DCAIDS, 25 WOA-GA, 34 and VOELA 32 in order to evaluate the performance in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection. The evaluation outcomes are explained below.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Abdan and Seno 26 established a machine learning models for the wormhole intrusion detection in MANET. The cluster trust adaptive acknowledgement based multi‐objective particle swarm optimization (CTAA‐MPSO) algorithm was implemented by Rajeshkumar et al 27 to determine unauthorized activities in MANET. The optimal number of clusters was selected by using an optimization algorithm and the synchronization errors were eliminated during data transmission using the kalman filter (KF).…”

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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“…Evaluation of the application effect of algorithm Actual scenario Accuracy (%) Recall (%) F1 value (%) Energy [24] 91% 93% 92% Medical treatment [25] 88% 89% 79% Network security [26] 89% 92% 81%…”

Section: Tablementioning

confidence: 99%

Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision

Yue,

Wang

2024

Decis. Mak. Appl. Manag. Eng.

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In the 21st century, the software industry has achieved great development. The development complexity and volume of software projects are also continuously increasing. The design of software engineering supervision network plans is becoming increasingly important. In response to the poor optimization performance and poor convergence and distribution of optimal solutions in existing network planning algorithms, the Pareto optimal solution set construction method, global extremum selection method, and fitness value determination method of multi-objective particle swarm optimization algorithm are improved to enhance the convergence and distribution of the algorithm. Traditional methods only optimize one or two objectives of network planning, resulting in inconsistency with actual engineering. A multi-objective model based on resources, duration, cost, and quality is established for comprehensive optimization. Based on the results, the Pareto optimal solution curves obtained by the proposed algorithm on three classic test functions are consistent with the actual theoretical Pareto frontier curves. The proposed method is applied to engineering project examples. 10 solutions that meet the schedule requirements are obtained. Most engineering projects have a quality of over 80%, which verifies the practicality of the algorithm. The algorithm has achieved good results in optimizing engineering quality. Therefore, this model has the ability to consider various indicators such as resources and costs to obtain software engineering quality improvement plans. It has certain application potential.

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

Cited by 21 publications

References 18 publications

Enhancing MANET Security: A Watch Dog Routing Algorithm Approach for Intruder and Black Hole Attack Detection

Enhancing MANET Security: A Watch Dog Routing Algorithm Approach for Intruder and Black Hole Attack Detection

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

Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision

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