Abstract
ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.
Original language | English (US) |
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Pages (from-to) | 179-212 |
Number of pages | 34 |
Journal | Evolutionary computation |
Volume | 13 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2005 |
Externally published | Yes |
Keywords
- Anomaly detection
- Artificial immune systems
- Computer security
- Immune system
- Machine learning
- Network intrusion detection
ASJC Scopus subject areas
- Computational Mathematics