An Optimized Compound Deep Neural Network Integrating With Feature Selection for Intrusion Detection System in Cyber Attacks

Document Type : Original Article

Authors

1 Associate Professor, Faculty of Electrical Engineering, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran

2 Assistant Professor, Command University and Aja Headquarters, Tehran, Iran

Abstract

In today's digital era, security issues and cyber attacks have become a serious and attention-needed concern as they hamper secured and vital information relating to organizations or individuals. Accordingly, timely detection of these vulnerabilities made by intruders is essential, wherein the cornerstone of security ensures the user's data privacy as an intrusion detection system (IDS). On the other hand, with the rapid development of machine learning (ML) and deep learning (DL) methods in the data world, one of their significant applications is dedicated to IDS using state-of-the-art classification algorithms, which has been the subject of numerous research to enhance accuracy and reliability in recent years. As a consequence, this paper presents a hybrid model integrating feature selection, classification, and hyper-parameters optimization. First, the initial massive features are subjected separately to the modified mutual information (MMI), genetic algorithm (GA), and Anova F-value approaches, followed by extracting the common outputs as optimal and reduced final features. Subsequently, a compound CNN and LSTM classifier (CNN-LSTM) is employed, where its hyper-parameters will be determined through a random switch grey wolf-whale optimization algorithm (RS-GWO-WOA) instead of a time-consuming trial and error manual process. Ultimately, to analyze the suggested scheme, a comparison with other strategies in terms of accuracy, precision, recall, F1 score, and periods of time on the NSL-KDD dataset has been accomplished, confirming the superiority of the developed approach.

Keywords

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References

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History

  • Receive Date: 04 January 2022
  • Revise Date: 20 February 2022
  • Accept Date: 09 August 2022
  • Publish Date: 21 January 2023

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