Open Access

ARTICLE

Wireless Intrusion Detection Based on Optimized LSTM with Stacked Auto Encoder Network

S. Karthic^1,*, S. Manoj Kumar²

1 Hindusthan College of Engineering and Technology, Coimbatore, 641032, India
2 KPR Institute of Engineering and Technology, Coimbatore, 641048, India

* Corresponding Author: S. Karthic. Email:

Intelligent Automation & Soft Computing 2022, 34(1), 439-453. https://doi.org/10.32604/iasc.2022.025153

Received 13 November 2021; Accepted 13 January 2022; Issue published 15 April 2022

Abstract

In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as input to the optimized LSTM which is used as an intrusion identification system. To enhance the effectiveness of the LSTM, we present the pigeon optimization algorithm (POA). Using this algorithm, weight parameters of the LSTM are chosen optimally. Finally, the proposed IDS model decides whether the input packets are intruded or not. The results confirm that the proposed IDS model surpasses the previous machine learning-based IDS models in terms of correctness, F1-score and G mean.

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Keywords

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