A Heuristic Detector Generation Algorithm for Negative Selection Algorithm with Hamming Distance Partial Matching Rule

2006 ◽  
pp. 229-243 ◽  
Author(s):  
Wenjian Luo ◽  
Zeming Zhang ◽  
Xufa Wang
Keyword(s):  
Negative Selection ◽  
Hamming Distance ◽  
Selection Algorithm ◽  
Matching Rule ◽  
Generation Algorithm ◽  
Partial Matching ◽  
Negative Selection Algorithm

Negative Selection Algorithm Based on Double Matching Rules

2011 ◽  
Vol 204-210 ◽  
pp. 42-45 ◽  
Author(s):  
Yu Hu ◽  
Bin Li
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Negative Selection ◽  
Hamming Distance ◽  
Network Intrusion Detection ◽  
Selection Algorithm ◽  
Matching Rule ◽  
Negative Selection Algorithm ◽  
Network Intrusion ◽  
Matching Rules

The theory of artificial immune had been widely used in the research of network intrusion detection. Nowadays, the existing detector generating algorithms based on negative selection usually use a certain matching rule, as a result, too many detectors may generate, and the false alarm rate will become more serious. This paper proposes an improved negative selection algorithm using double matching rule: candidate detectors should be selected by the improved Hamming distance matching first, then the remaining detectors go through the segmented r-chunks(rch) matching rule. Experiments show that compared with traditional algorithms, this method brings a small number and more efficient detectors, reduces the false alarm rate and guarantees the efficiency of detectors.

NUMERICAL NEGATIVE SELECTION ALGORITHM

2010 ◽  
Vol 19 (05) ◽  
pp. 703-712
Author(s):  
TAO CAI ◽  
SHIGUANG JU ◽  
DEJIAO NIU
Keyword(s):  
Negative Selection ◽  
Detection System ◽  
Immune Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Time And Space ◽  
Data Set ◽  
Matching Rule ◽  
Artificial Immune Algorithm ◽  
Negative Selection Algorithm

The artificial immune algorithm is the hot topic in much research such as the intrusion detection system, the information retrieval system and the data mining system. The negative selection algorithm is the typical artificial immune algorithm. A common representation of binary strings for antibody (detector) and antigen have been associated with inefficiencies when generating detector and inspecting antigen. We use a single integer to represent the detector and provide the basis of improving negative selection algorithm efficiency. In the detector generation algorithm, extracting sub-strings in self that its length is larger than threshold and converting them to single integer in numerical interval, then the rest integers in numerical interval are selected as numerical detectors. It can reduce the time and space overhead of detector generation and provide the facility to analyze the positive and negative errors when antigen inspection. The numerical matching rule is given. The B-tree is used to create index of numerical detector. Extracting sub-strings in antigen that its length is larger than threshold and converting them to some integers. If there is the same value as those integers in the index of numerical detector, then the antigen matches one numerical detector. It can improve the efficiency of antigen inspection. Finally the prototype of the numerical negative selection algorithm and negative selection algorithm are realized to test the overhead of the detector generation and antigen inspection using the live data set. The results show that the numerical negative selection algorithm can reduce the time and space overhead and avoid fluctuation of the overhead.

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