Open Access

ARTICLE

An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

by A. Arivazhagi^1,*, S. Raja Kumar²

1 Department of Computer Science and Engineering, University College of Engineering, Ariyalur, Tamilnadu, India
2 Department of Mathematics, University College of Engineering, Ariyalur, Tamilnadu, India

* Corresponding Author: A. Arivazhagi. Email:

Computer Systems Science and Engineering 2022, 43(1), 141-157. https://doi.org/10.32604/csse.2022.021851

Received 17 July 2021; Accepted 06 September 2021; Issue published 23 March 2022

Abstract

Intelligent Intrusion Detection System (IIDS) for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall. The efficiency of IIDS highly relies on the algorithm performance. The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms. Here, a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework (SILF), is proposed to learn the attack features and reduce the dimensionality. It also reduces the testing and training time effectively and enhances Linear Support Vector Machine (l-SVM). It constructs an auto-encoder method, an efficient learning approach for feature construction unsupervised manner. Here, the inclusive certified signature (ICS) is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers. By training the samples in the preliminary stage, the selected features are provided into the classifier (lSVM) to enhance the prediction ability for intrusion and classification accuracy. Thus, the model efficiency is learned linearly. The multi-classification is examined and compared with various classifier approaches like conventional SVM, Random Forest (RF), Recurrent Neural Network (RNN), STL-IDS and game theory. The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy, precision, recall, F-measure, p-value, MCC and so on. The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection. Here, the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

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Keywords

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