Molecular Swarm Optimization Analysis of Data Transmission and Recurrent Neural Networks (RNNs) for Attack Prevention in Mobile Ad Hoc Networks (MANETs)

• Ahmed Ayad Abdalhameed,
• Ammar Ismael Kadhim,

Vol. 19, No. 3, March 31, 2025
10.3837/tiis.2025.03.014, Download Paper (Free):

• MANET network
• Particle Swarm Optimization
• replicator neural network
• Routing Protocols

Abstract

In this paper, the use of molecular swarm optimization (PSO) and recurrent neural networks (RNN) is studied to improve security and efficiency. In mobile ad hoc networks (MANET), an objective is developed to use an improved algorithm to improve data transmission and defense against attacks, as well as to use recurrent red neurons to improve the intrusion detection system. A new technique based on mobile technology examples combined with AODV (Ad hoc On Demand Distance Vector) input protocol has been investigated in this work to improve the performance of red. The results show that the focus contributes to improving the data transmission efficiency by 20% and the attack detection by 30% compared to existing techniques. Improve both the effectiveness of the intrusion detection system and the security of mobile ad hoc networks (MANET) using advanced techniques is the main subject of this paper. Additionally, MANETs security and efficiency was studied by applying Particle Swarm Optimization and Recurrent Neural Networks, where proposed approach has been developed depending on animated randomization algorithm. This approach improves data transmission and provides defense against attacks, in addition to enhancement in intrusion detection system by repetitive neural networks. The research helps providing system analysis, data transmission algorithms and system defense. It presents comprehensive study of iterative neural networks, animated randomization technology, and the AODV routing protocol. The literature was also reviewed, a study was conducted and conclusions were presented. The results have shown that the use of animated random examples technology contributes to improving the efficiency of data transfer within the network by identifying the optimal and most efficient paths from source to destination. Traffic features collected in the network are used to both classify and identify different types of threats. Centralized authority and mobility of MANETs are unique features; however they also open security gaps. The approach proposed in this paper provides techniques to protect the network from untrusted nodes and increase its security using sharing technology and improve data transmission and reception using animated random example technology. This study aims to improve the effectiveness of intrusion detection system, improve data transmission, and enhance the security of temporary mobile networks (MANETs) through the use of advanced technologies. Data transmission efficiency is improved by 20% and attack detection time is reduced by 30%, improving the security and overall performance of the temporary mobile network. Integrating the previous mentioned technologies enables system to identify the best data transmission routes, prevent network attacks, and improve the overall performance of temporary mobile networks. This contribution is innovative because it uses an improved randomization algorithm combined with the AODV protocol to efficiently route data and protect the network from attacks. The results show significant improvements in efficiency by 20% and in attack detection by 30%, compared to existing techniques, enhancing the performance and security of mobile networks.

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