Tu, Shanshan, Waqas, Muhammad ORCID: 0000-0003-0814-7544 , Badshah, Akhtar, Yin, Mingxi and Abbas, Ghulam (2023) Network Intrusion Detection System (NIDS) based on Pseudo-Siamese Stacked Autoencoders in fog computing. IEEE Transactions on Service Computing, 16 (6). pp. 4317-4327. ISSN 1939-1374 (Online) (doi:https://doi.org/10.1109/TSC.2023.3319953)

Preview

PDF (AAM) 44487_WAQAS_Network_Intrusion_Detection_System_NIDS_based_on_pseudo_Siamese_stacked_autoencoders.pdf - Accepted Version Download (701kB) | Preview

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 capa- bilities. 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.

Item Type:	Article
Uncontrolled Keywords:	intrusion detection; IoT; fog computing; autoencoder; pseudo-siamese neural network
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	21 Dec 2023 14:33
URI:	http://gala.gre.ac.uk/id/eprint/44487

Actions (login required)

View Item

Downloads