Name
Abstract | ||
|---|---|---|
Machine learning techniques have been actively studied and achieved significant performance improvements in various kinds of tasks. While we benefit from such techniques in many ways, they can be a serious security threat to the Internet if malicious attackers become able to utilize them to discover unknown software vulnerabilities. This paper introduces a new concept of self-evolving botnets, where computing resources of infected hosts are exploited to discover unknown vulnerabilities in non-infected hosts and the botnets evolve autonomously. We provide a stochastic epidemic model for the self-evolving botnets, and show its behaviors through numerical and simulation experiments. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.comcom.2018.04.010 | Computer Communications |
Keywords | Field | DocType |
Botnet,Computer virus,Machine learning,Continuous-time Markov chain | Epidemic model,Computer science,Computer security,Botnet,Computer network,Software,Vulnerability discovery,Abstract machine,The Internet,Vulnerability | Journal |
Volume | ISSN | Citations |
124 | 0140-3664 | 0 |
PageRank | References | Authors |
0.34 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takanori Kudo | 1 | 1 | 1.73 |
| Tomotaka Kimura | 2 | 11 | 8.82 |
| Yoshiaki Inoue | 3 | 3 | 3.92 |
| Hirohisa Aman | 4 | 24 | 10.74 |
| Kouji Hirata | 5 | 10 | 11.28 |