Document Type

Journal Article

Publication Title

Computers, Materials and Continua

Volume

74

Issue

1

First Page

801

Last Page

815

Publisher

Tech Science Press

School

School of Engineering

RAS ID

53016

Funders

Beijing Natural Science Foundation (No. 4212015) / Natural Science Foundation of China (No. 61801008) / China Ministry of Education-China Mobile Scientific Research Foundation (No. MCM20200102) / China Postdoctoral Science Foundation (No. 2020M670074) / Beijing Municipal Commission of Education Foundation (No. KM201910005025) / Deanship of Scientific Research at King Khalid University (Grant Number RGP.2/201/43)

Comments

Yang, Y., Tu, S., Ali, R. H., Alasmary, H., Waqas, M., & Amjad, M. N. (2023). Intrusion detection based on bidirectional long short-term memory with attention mechanism. CMC-Computer Material and Continua, 74(1), 801-815. https://doi.org/10.32604/cmc.2023.031907

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.

DOI

10.32604/cmc.2023.031907

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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