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Author:

Tu, Shanshan (Tu, Shanshan.) | Waqas, Muhammad (Waqas, Muhammad.) | Badshah, Akhtar (Badshah, Akhtar.) | Yin, Mingxi (Yin, Mingxi.) | Abbas, Ghulam (Abbas, Ghulam.)

Indexed by:

EI Scopus SCIE

Abstract:

The proliferation of Internet of Things (IoT) devices in the 5G era has resulted in increased security vulnerabilities and zero-day attacks, underscoring the importance of network intrusion detection systems (NIDS). However, existing NIDS have limitations in terms of accuracy, recall rates, false alarm rates, and generalization capabilities, and they cannot meet the IoT's requirements for low latency and limited computing resources. To overcome these challenges, we propose a NIDS based on a pseudo-siamese stacked autoencoder (PSSAE), deployed in the fog computing layer. Our system uses unsupervised training of stacked autoencoders (SAEs) to extract deep semantic features of normal and abnormal traffic, followed by supervised learning with labels to improve characterization and classification capabilities. The results show that our proposed method's accuracy and detection rate (DR) is 2% to 15% and 1%-14% higher than the existing techniques using the KDDTest+ dataset, respectively. Our proposed method outperformed the existing methods by 1% to 4% using the KDDTest+ dataset. The F1-Score is higher by 3%-11.55% using the KDDTest+ dataset. On the other hand, using the KDDTest-21 dataset, the accuracy of our proposed method also outperformed the existing technique by 6.09%-13.81%. The DR and F1-Score are higher by 7.02% and 5.57%, respectively, using the KDDTest+ dataset. This is due to the fact that each layer of the network trained by SAEs is more capable of extracting the semantic features of the data than the DNN-trained network directly.

Keyword:

autoencoder Intrusion detection fog computing pseudo-siamese neural network IoT

Author Community:

  • [ 1 ] [Tu, Shanshan]Beijing Univ Technol, Fac Informat Technol, Engn Res Ctr Intelligent Percept & Autonomous Cont, Beijing 100021, Peoples R China
  • [ 2 ] [Yin, Mingxi]Beijing Univ Technol, Fac Informat Technol, Engn Res Ctr Intelligent Percept & Autonomous Cont, Beijing 100021, Peoples R China
  • [ 3 ] [Waqas, Muhammad]Univ Greenwich, Fac Engn & Sci, Ctr Sustainable Cyber Secur, Sch Comp & Math Sci, London SE10 9LS, England
  • [ 4 ] [Waqas, Muhammad]Edith Cowan Univ, Schoolof Engn, Perth, WA 6027, Australia
  • [ 5 ] [Badshah, Akhtar]Univ Malakand, Dept Software Engn, Chakdara 18800, Khyber Pakhtunk, Pakistan
  • [ 6 ] [Abbas, Ghulam]Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Comp Sci & Engn, Topi 23460, Pakistan

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Source :

IEEE TRANSACTIONS ON SERVICES COMPUTING

ISSN: 1939-1374

Year: 2023

Issue: 6

Volume: 16

Page: 4317-4327

8 . 1 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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