Open Access

ARTICLE

WOA-DNN for Intelligent Intrusion Detection and Classification in MANET Services

C. Edwin Singh^1,*, S. Maria Celestin Vigila²

1 Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Nagercoil, 629180, India
2 Department of Information Technology, Associate Professor, Noorul Islam Centre for Higher Education, Nagercoil, 629180, India

* Corresponding Author: C. Edwin Singh. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1737-1751. https://doi.org/10.32604/iasc.2023.028022

Received 31 January 2022; Accepted 09 March 2022; Issue published 19 July 2022

Abstract

Mobile ad-hoc networks (MANET) are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communications. MANETs are more vulnerable to security threats. Changes in nodes, bandwidth limits, and centralized control and management are some of the characteristics. IDS (Intrusion Detection System) are the aid for detection, determination, and identification of illegal system activity such as use, copying, modification, and destruction of data. To address the identified issues, academics have begun to concentrate on building IDS-based machine learning algorithms. Deep learning is a type of machine learning that can produce exceptional outcomes. This study proposes that WOA-DNN be used to detect and classify incursions in MANET (Whale Optimized Deep Neural Network Model) WOA (Whale Optimization Algorithm) and DNN (Deep Neural Network) are used to optimize the preprocessed data to construct a system for classifying and predicting unanticipated cyber-attacks that are both effective and efficient. As a result, secure data transport to other nodes is provided, preventing intruder attacks. The invaders are found using the (Machine Learning) ML-IDS and WOA-DNN methods. The data is reduced in dimensionality using Principal Component Analysis (PCA), which improves the accuracy of the outputs. A classifier is used in forward propagation to predict whether a result is normal or malicious. To compare the traditional and proposed models’ effectiveness, the accuracy of classification, detection of the attack rate, precision rate, and F-Measure, Recall are utilized. The proposed WOA-DNN model has higher assessment metrics and a 99.1% accuracy rate. WOA-DNN also has a greater assault detection rate than others, resulting in fewer false alarms. The classification accuracy of the proposed WOA-DNN model is 99.1%.

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Keywords

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