Name
Abstract | ||
|---|---|---|
Traditional behavior-based worm detection can't eliminate the influence of the worm-like P2P traffic effectively, as well as detect slow worms. To try to address these problems, this paper first presents a user habit model to describe the factors which influent the generation of network traffic, then a design of HPBRWD (Host Packet Behavior Ranking Based Worm detection) and some key issues about it are introduced. This paper has three contributions to the worm detection: 1) presenting a hierarchical user habit model; 2) using normal software and time profile to eliminate the worm-like P2P traffic and accelerate the detection of worms; 3) presenting HPBRWD to effectively detect worms. Experiments results show that HPBRWD is effective to detect worms. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-88873-4_4 | OTM Conferences (2) |
Keywords | Field | DocType |
p2p traffic,worm detection,host packet behavior ranking,slow worm,host packet behavior,user habit model,novel worm detection model,experiments result,network traffic,traditional behavior-based worm detection,hierarchical user habit model,key issue,p2p | Ranking,Biology,Computer security,Network packet,Computer network,Software,Time profile | Conference |
Volume | ISSN | Citations |
5332 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengtao Xiao | 1 | 3 | 1.44 |
| Huaping Hu | 2 | 35 | 7.92 |
| Bo Liu | 3 | 63 | 12.54 |
| Xin Chen | 4 | 3 | 1.44 |