Automated Big Traffic Analytics for Cyber Security
Yuantian Miao, Zichan Ruan, Lei Pan, Yu Wang, Jun Zhang, Yang Xiang

TL;DR
This paper reviews advanced techniques for automated big traffic analytics in cyber security, focusing on real-time classification, unknown traffic detection, and classifier efficiency to enhance intrusion detection, malware analysis, and botnet detection.
Contribution
It introduces new statistical, discovery, and correlation techniques tailored for big traffic data challenges in cyber security applications.
Findings
Promising potential of new techniques for big traffic data analysis
Effective real-time traffic classification methods
Improved detection of unknown traffic types
Abstract
Network traffic analytics technology is a cornerstone for cyber security systems. We demonstrate its use through three popular and contemporary cyber security applications in intrusion detection, malware analysis and botnet detection. However, automated traffic analytics faces the challenges raised by big traffic data. In terms of big data's three characteristics --- volume, variety and velocity, we review three state of the art techniques to mitigate the key challenges including real-time traffic classification, unknown traffic classification, and efficiency of classifiers. The new techniques using statistical features, unknown discovery and correlation analytics show promising potentials to deal with big traffic data. Readers are encouraged to devote to improving the performance and practicability of automatic traffic analytic in cyber security.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNetwork Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Advanced Malware Detection Techniques
