A survey of cloud-based network intrusion detection analysis
Abstract
As network traffic grows and attacks become more prevalent and complex, we must find creative new ways to enhance intrusion detection systems (IDSes). Recently, researchers have begun to harness both machine learning and cloud computing technology to better identify threats and speed up computation times. This paper explores current research at the intersection of these two fields by examining cloud-based network intrusion detection approaches that utilize machine learning algorithms (MLAs). Specifically, we consider clustering and classification MLAs, their applicability to modern intrusion detection, and feature selection algorithms, in order to underline prominent implementations from recent research. We offer a current overview of this growing body of research, highlighting successes, challenges, and future directions for MLA-usage in cloud-based network intrusion detection approaches.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2016
- Source ID
- 10.1186/s13673-016-0076-z
Entities
People
- Aastha Chaudhary
- Byunggu Yu
- Claude Concolato
- Dong Hyun Jeong
- Nathan Keegan
- Soo-Yeon Ji
Organizations
- Army Research Office