Name
Abstract | ||
|---|---|---|
The increasing size, variety, rate of growth and change, and complexity of network data has warranted advanced network analysis and services. Tools that provide automated analysis through traditional or advanced signature-based systems or machine learning classifiers suffer from practical difficulties. These tools fail to provide comprehensive and contextual insights into the network when put to practical use in operational cyber security. In this paper, we present an effective tool for network security and traffic analysis that uses high-performance data analytics based on a class of unsupervised learning algorithms called tensor decompositions. The tool aims to provide a scalable analysis of the network traffic data and also reduce the cognitive load of network analysts and be network-expert-friendly by presenting clear and actionable insights into the network. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.future.2019.01.039 | Future Generation Computer Systems |
Keywords | Field | DocType |
Network analysis,Cyber security,Tensor decompositions,Network threats | Traffic analysis,Security operations center,Computer security,Computer science,Network security,Unsupervised learning,Local area network,Network analysis,Internet Control Message Protocol,Scalability,Distributed computing | Journal |
Volume | ISSN | Citations |
96 | 0167-739X | 2 |
PageRank | References | Authors |
0.45 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muthu Manikandan Baskaran | 1 | 493 | 33.10 |
| Thomas Henretty | 2 | 79 | 5.15 |
| James R. Ezick | 3 | 17 | 3.60 |
| Richard Lethin | 4 | 118 | 17.17 |
| David Bruns-Smith | 5 | 10 | 1.60 |