Name
Abstract | ||
|---|---|---|
Botnet malware, which infects Internet-connected devices and seizes control for a remote botmaster, is a long-standing threat to Internet-connected users and systems. Botnets are used to conduct DDoS attacks, distributed computing (e.g., mining bitcoins), spread electronic spam and malware, conduct cyberwarfare, conduct click-fraud scams, and steal personal user information. Current approaches to the detection and classification of botnet malware include syntactic, or signature-based, and semantic, or context-based, detection techniques. Both methods have shortcomings and botnets remain a persistent threat. In this paper, we propose a method of botnet detection using Nonparametric Bayesian Methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3098954.3107010 | ARES |
Keywords | Field | DocType |
Botnets, Cybersecurity, Nonparametric Bayesian Methods | Cutwail botnet,ZeroAccess botnet,Rustock botnet,Web threat,Computer security,Srizbi botnet,Computer science,Botnet,Asprox botnet,Malware | Conference |
ISBN | Citations | PageRank |
978-1-4503-5257-4 | 0 | 0.34 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph DiVita | 1 | 3 | 0.89 |
| Roger Hallman | 2 | 14 | 7.73 |