DeepAI AI Chat
Log In Sign Up

Automated Big Traffic Analytics for Cyber Security

by   Yuantian Miao, et al.
Swinburne University of Technology
Deakin University
Guangzhou University

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.


A short review on Applications of Deep learning for Cyber security

Deep learning is an advanced model of traditional machine learning. This...

On the Scalability of Big Data Cyber Security Analytics Systems

Big Data Cyber Security Analytics (BDCA) systems use big data technologi...

Large Scale Enrichment and Statistical Cyber Characterization of Network Traffic

Modern network sensors continuously produce enormous quantities of raw d...

Using Google Analytics to Support Cybersecurity Forensics

Web traffic is a valuable data source, typically used in the marketing s...

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

The world is witnessing an unprecedented growth of cyber-physical system...

Hyperscaling Internet Graph Analysis with D4M on the MIT SuperCloud

Detecting anomalous behavior in network traffic is a major challenge due...

Cloud Based Big Data DNS Analytics at Turknet

Domain Name System (DNS) is a hierarchical distributed naming system for...