An adapting soft computing model for intrusion detection system
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Network security in smart cities has become a key problem in the rapid development of computer networks over the past few years. Intrusion detection systems play a fundamental part in the integrity, confidentiality, and resource accessibility among the multiple network security policies. The classification of the genuineness of packets is main object of the presents research work, the soft computing has applied to classify the genuineness of packets. The complexity of soft computing is greatly reduced if the numbers of features in a dataset are reduced. Managing and analysis of the dimensionality reduction is novelty of the proposed model. The existence of uncertainty and the imprecise nature of the intrusions appear to create suitable fuzzy logic systems for such structures. The neural-fuzzy algorithm is one of the effective methods that incorporate fuzzy logic systems into adaptive and analysis capacities. In this research work, soft computing fuzzy logic system is proposed to enhance network security through intrusion detection. Three network datasets are demonstrated to test and estimate the proposed system. Feature selection has used to remove irrelevant features from entire network data which are obstacle classification processes. The Information Gain method was applied to select importance features for detection intrusion. Adaptive Neuro-Fuzzy Inference System (ANFIS) is further used to process the significant features of the classification network data as normal or attacks packets. Two functions named Jang's Neuro-fuzzy and faster-scaled conjugate gradient (SCG) based on the ANFIS system. Obviously, the experimental results demonstrate the proposed system has attained higher precision in detecting normal or attack. The experimental results have suggested that the proposed system results are better in accuracy and time process for classification compared with the existing models. The Overall Results show that the proposed system can be able to detect various intrusions efficiently and effectively.