GAN-based intrusion detection model using MLP encoder

Xiao Zhang; Xiaoming Chen; Yuxiong He; Youhuai Wang; Yong Cai; Huixun Li

doi:10.1117/12.2685912

28 July 2023 GAN-based intrusion detection model using MLP encoder

Xiao Zhang, Xiaoming Chen, Yuxiong He, Youhuai Wang, Yong Cai, Huixun Li

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127564T (2023) https://doi.org/10.1117/12.2685912
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

As the number of network attacks continues to rise, effective intrusion detection systems (IDSs) have become crucial for maintaining network security. Traditional machine learning (ML) methods have achieved good results in intrusion detection, but are often limited by insufficient training data and vulnerability to adversarial attacks. To address these limitations, we propose a neural network model based on Generative Adversarial Networks (GAN). GAN have achieved remarkable success in detecting such adversarial attacks, mainly due to the adversarial training of generators and discriminators in an attempt to bypass each other and thereby improve their own capabilities and accuracy. Furthermore, the generator of GANs can generate fake samples that are similar to real data, thereby alleviating the problem of insufficient training data. Our experimental results demonstrate that our proposed IDS model effectively detects network attacks with high accuracy. We believe that this approach represents a promising direction for future research in the field of network security.

Citation Download Citation

Xiao Zhang, Xiaoming Chen, Yuxiong He, Youhuai Wang, Yong Cai, and Huixun Li "GAN-based intrusion detection model using MLP encoder", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127564T (28 July 2023); https://doi.org/10.1117/12.2685912

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available