Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator

2002 ◽  
Author(s):  
Jungwon Kim ◽  
P.J. Bentley
Keyword(s):  
Immune System ◽  
Intrusion Detection ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Network Intrusion Detection ◽  
Artificial Immune ◽  
Network Intrusion ◽  
Selection Operator

scholarly journals A Machine Learning Evaluation of an Artificial Immune System

2005 ◽  
Vol 13 (2) ◽  
pp. 179-212 ◽  
Author(s):  
Matthew Glickman ◽  
Justin Balthrop ◽  
Stephanie Forrest
Keyword(s):  
Machine Learning ◽  
Immune System ◽  
Intrusion Detection ◽  
Artificial Immune System ◽  
Concept Drift ◽  
Network Intrusion Detection ◽  
Artificial Immune ◽  
Data Set ◽  
Network Intrusion ◽  
Adaptive Mechanisms

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.

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