A novel fuzzy anomaly detection algorithm based on artificial immune system

2005 ◽  
Author(s):  
Li Zhi-tang ◽  
Li Yao ◽  
Wang Li
Keyword(s):  
Immune System ◽  
Anomaly Detection ◽  
Artificial Immune System ◽  
Detection Algorithm ◽  
Artificial Immune

scholarly journals Detecting Mixed-Type Intrusion in High Adaptability Using Artificial Immune System and Parallelized Automata

2020 ◽  
Vol 10 (5) ◽  
pp. 1566
Author(s):  
Fu-I Chou ◽  
Wen-Hsien Ho ◽  
Yenming J. Chen ◽  
Jinn-Tsong Tsai ◽  
Chia-Wen Chang
Keyword(s):  
Immune System ◽  
Mixed Type ◽  
Artificial Immune System ◽  
Detection Algorithm ◽  
Artificial Immune ◽  
Evolutionary Patterns ◽  
Testing Data ◽  
Finite State ◽  
Selection Step ◽  
Massive Number

This study applies artificial immune system and parallelized finite-state machines to construct an intrusion detection algorithm for spotting hidden threats in massive number of packets. Existing intrusion detections are mostly not focused on adaptability for mixed and changing attacks, which results in low detection rate in new and mixed-type attacks. Using the characteristics of artificial immune and state transition can address the attacks in evolutionary patterns and track the anomalies in nonconsecutive packets. The proposed immune algorithm in this study is highly efficient based on a selection step in multi-island migration. Result shows that the algorithm can effectively detect mixed-type attacks and obtains an overall accuracy of 95.9% in testing data.

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