A Hybrid Nested Genetic-Fuzzy Algorithm Framework for Intrusion Detection and Attacks

Intrusion Detection System (IDS) plays a very important role in security systems. Among its different types, Network Intrusion Detection System (NIDS) has an effective role in monitoring computer networks systems for malicious and illegal activities. In the literature, the detection of DoS and Probe...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.98218-98233
Main Authors: Elhefnawy, Ramy, Abounaser, Hassan, Badr, Amr
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Biological cells
Categories
classification algorithms
Computer networks
Datasets
evolutionary computation
Fuzzy logic
fuzzy logic systems
Fuzzy sets
Genetic algorithms
hybrid intelligent systems
Intrusion detection
Intrusion detection system
Intrusion detection systems
Model accuracy
Optimization
Security systems
System effectiveness
Uncertainty
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Summary:Intrusion Detection System (IDS) plays a very important role in security systems. Among its different types, Network Intrusion Detection System (NIDS) has an effective role in monitoring computer networks systems for malicious and illegal activities. In the literature, the detection of DoS and Probe attacks were with reasonable accuracy in most of the NIDS researches. However, the detection accuracy of other categories of attacks is still low, such as the R2L and U2R in KDDCUP99 dataset along with the Backdoors and Worms in UNSW-NB15 dataset. Computational Intelligence (CI) techniques have the characteristics to address such imprecision problem. In this research, a Hybrid Nested Genetic-Fuzzy Algorithm (HNGFA) framework has been developed to produce highly optimized outputs for security experts in classifying both major and minor categories of attacks. The adaptive model is evolved using two-nested Genetic-Fuzzy Algorithms (GFA). Each GFA consists of two-nested Genetic Algorithms (GA). The outer is to evolve fuzzy sets and the inner is to evolve fuzzy rules. The outer GFA assists the inner GFA in training phase, where the best individual in outer GFA interacts with the weak individual in inner GFA to generate new solutions that enhance the prediction of mutated attacks. Both GFA interact together to evolve the best rules for normal, major and minor categories of attacks through the optimization process. Several experiments have been conducted with different settings over different datasets. The obtained results show that the developed model has good accuracy and is more efficient compared with several state-of-the-art techniques.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2996226