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Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm
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DOI：10.4018/IJSWIS.307324.png)
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Abstract
En 中文
This paper presents a detection algorithm using normalized mutual information feature selection and cooperative evolution of multiple operators based on adaptive parallel quantum genetic algorithm (NMIFS MOP-AQGA). The proposed algorithm is to address the problems that the intrusion detection system (IDS) has lower detection speed, less adaptability, and lower detection accuracy. In order to achieve an effective reduction for high-dimensional feature data, the NMIFS method is used to select the best feature combination. The best features are sent to the MOP-AQGA classifier for learning and training, and the intrusion detectors are obtained. The data are fed into the detection algorithm to ultimately generate accurate detection results. The experimental results on real abnormal data demonstrate that the NMIFS MOP-AQGA method has higher detection accuracy, lower false negative rate, and higher adaptive performance than the existing detection methods, especially for small samples sets.
Keywords:
Entropy
Features Reduction
Genetic Algorithm
Intrusion Detection
Mutual Information Feature Selection
Operator
Parallel Universe
Quantum
Journal
I
IF:
5.6
Papers:
471
Citations:
914