Attention-based deep learning for network intrusion detection

Naiwang Guo; Yingjie Tian; Fan Li; Hongshan Yang

doi:10.1117/12.2579300

10 November 2020 Attention-based deep learning for network intrusion detection

Naiwang Guo, Yingjie Tian, Fan Li, Hongshan Yang

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841I (2020) https://doi.org/10.1117/12.2579300
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

The computer network has been widely used in various industries of society, and network security has received unprecedented attention. Network intrusion detection technology is the critical technologies, which can maintain network security. However, the traditional rule-based intrusion detection method has some shortcomings, such as relying on manual intervention, and it is difficult to update the rule database in real-time. Therefore, in this paper, we propose a novel network intrusion detection model based on deep attention neural network. In particular, we combine the LSTM, multi-layer perception and the attention mechanism in an end-to-end model in order to extract features automatically by deep learning technologies. Finally, we conduct extensive experiments on the KDD99 and NSL-KDD dataset, and the results demonstrate the effectiveness of our proposed approach.

Citation Download Citation

Naiwang Guo, Yingjie Tian, Fan Li, and Hongshan Yang "Attention-based deep learning for network intrusion detection", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841I (10 November 2020); https://doi.org/10.1117/12.2579300

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