Abstract
In this study we model the sequences and time intervals of online intrusion behaviors. To maintain network security, intrusion detection systems monitor network environments; however, most existing intrusion detection systems produce too many intrusion alerts, causing network managers to investigate many potential intrusions individually to determine their validity. To solve this problem, we combined a clustering analysis of the time intervals of online users' behaviors with a sequential pattern analysis to identify genuine intrusion behaviors. Knowledge of the patterns generated by intruder behaviors can help network managers maintain network security.
| Original language | English |
|---|---|
| Pages (from-to) | 1307-1312 |
| Number of pages | 6 |
| Journal | Social Behavior and Personality |
| Volume | 38 |
| Issue number | 10 |
| DOIs | |
| State | Published - Nov 2010 |
Keywords
- Intrusion behaviors
- Intrusion detection system
- Network security
- Sequential pattern analysis