Open Access

ARTICLE

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Yongjie Yang¹, Shanshan Tu¹, Raja Hashim Ali², Hisham Alasmary^3,4, Muhammad Waqas^5,6,*, Muhammad Nouman Amjad⁷
1 Engineering Research Center of Intelligent Perception and Autonomous Control, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
2 Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan
3 Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
4 Information Security and Cybersecurity Unit, King Khalid University, Abha, Saudi Arabia
5 Computer Engineering Department, College of Information Technology, University of Bahrain, 32038, Bahrain
6 School of Engineering, Edith Cowan University, Joondalup Perth, WA, 6027, Australia
7 School of Engineering, University of Management and Technology, Lahore, Pakistan
* Corresponding Author: Muhammad Waqas. Email:

Computers, Materials & Continua 2023, 74(1), 801-815. https://doi.org/10.32604/cmc.2023.031907

Received 29 April 2022; Accepted 24 June 2022; Issue published 22 September 2022

Abstract

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score.

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Keywords

Fog computing; intrusion detection; bi-LSTM; attention mechanism

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