Paper Info
Title
Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system
Abstract
This paper presents an approach for detecting network traffic anomalies using detectors generated by a genetic algorithm with deterministic crowding Niching technique. Particularly, the suggested approach is inspired by the negative selection mechanism of the immune system that can detect foreign patterns in the complement (non-self) space. In our paper, we run a number of experiments on the relatively new NSL-KDD data set which was never tested against this algorithm before our work. We run the test using different values for the involved parameters, to find out which values give the best detection rates, so we can give recommendations for future application of the algorithm. Also, Formal Concept Analysis is applied on the generated rules to visualize the relation among attributes. We will show in the results that the algorithm have very good results through the analysis, compared to other machine learning approaches.
Year
Venue
Keywords
2012
Computer Science and Information Systems
data mining,formal concept analysis,genetic algorithms,security of data,NSL-KDD data set,data mining research methods,detectors generation,deterministic crowding niching technique,foreign pattern detection,formal concept analysis,genetic algorithm,immune system,negative selection inspired anomaly network intrusion detection system,network traffic anomaly detection
Field
DocType
ISSN
Data mining,Network intrusion detection,Negative selection,Algorithm design,Computer science,Anomaly-based intrusion detection system,Artificial intelligence,Detector,Intrusion detection system,Formal concept analysis,Genetic algorithm,Machine learning
Conference
2325-0348
ISBN
Citations 
PageRank 
978-83-60810-51-4
5
0.49
References 
Authors
6
4
Name
Order
Citations
PageRank
Amira Sayed A. Aziz150.49
Mostafa A. Salama250.49
ella Hassanien, A.3364.57
Sanaa El-Ola Hanafi450.49