CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12332 > Article
Paper
6 September 2022 Intrusion detection using deep learning on packet data
Min Sun, Ruiyao Han
Author Affiliations +
Proceedings Volume 12332, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022); 123320E (2022) https://doi.org/10.1117/12.2652474
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022), 2022, Chengdu, China
Abstract
Deep learning has made good progress in intrusion detection field due to its powerful adaptive learning capability, but deep learning computes more and more slowly as model layers deepen, and existing deep learning models rely on datasets with artificially designed features, which in turn makes the models need to be deepened to learn the complete features, such data also makes it difficult to generalize the trained model. To solve these problems, we propose a method for extracting packet bytes from PCAP files as features to enable the models to learn the original features. Then propose a deep learning model IDPnet, which mainly employs one dimensional depthwise separable convolution, it regards each packet as channels, learns features through depthwise and pointwise convolution hierarchically, which greatly reduces the inference time while improving the accuracy compared to common CNN models that using converted grayscale image as input. Finally, we conduct transfer learning on IoT dataset, demonstrating that pre-trained model can be reused on data from different devices and protocols using packet data as features, reducing training time.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Min Sun and Ruiyao Han "Intrusion detection using deep learning on packet data", Proc. SPIE 12332, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022), 123320E (6 September 2022); https://doi.org/10.1117/12.2652474
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Convolution
Computer intrusion detection
Network security
Machine learning
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top