Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks

An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. The mobile users are enrolled with Trusted Authority Using a Crypto Hash Signature (SHA-256). Every mobi...

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Vydáno v:	Network (Bristol) Ročník 36; číslo 3; s. 426 - 450
Hlavní autoři:	Shanmugham, E.V.R.M. Kalaimani, Dhatchnamurthy, Saravanan, Pakkiri, Prabbu Sankar, Garg, Neha
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England 03.07.2025
Témata:	Algorithms Computer Security Humans Neural Networks, Computer NSL-KDD dataset MANET Cyber security attacks Adaptive activation Functions with Deep Kronecker Neural Network Crypto Hash Signature Geodesic filtering Group Teaching Algorithm adaptive density-based spatial clustering
ISSN:	0954-898X, 1361-6536, 1361-6536
On-line přístup:	Získat plný text
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Popis
Shrnutí:	An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. The mobile users are enrolled with Trusted Authority Using a Crypto Hash Signature (SHA-256). Every mobile user uploads their finger vein biometric, user ID, latitude and longitude for confirmation. The packet analyser checks if any attack patterns are identified. It is implemented using adaptive density-based spatial clustering (ADSC) that deems information from packet header. Geodesic filtering (GF) is used as a pre-processing method for eradicating the unsolicited content and filtering pertinent data. Group Teaching Algorithm (GTA)-based feature selection is utilized for ideal collection of features and Adaptive Activation Functions along Deep Kronecker Neural Network (ADKNN) is used to categorizing normal and attack packets (DoS, Probe, U2R, and R2L). Then BSSA is utilized for optimizing the weight parameters of ADKNN classifier for optimal classification. The proposed technique is executed in python and its efficiency is evaluated by several performances metrics, such as Accuracy, Attack Detection Rate, Detection Delay, Packet Delivery Ratio, Throughput, and Energy Consumption. The proposed technique provides 36.64%, 33.06%, and 33.98% lower Detection Delay on NSL-KDD dataset compared with the existing methods.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0954-898X 1361-6536 1361-6536
DOI:	10.1080/0954898X.2024.2321391