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.

中文翻译：

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一种适应入侵检测系统的软计算模型

智慧城市的网络安全已成为过去几年计算机网络快速发展的关键问题。入侵检测系统在多个网络安全策略中的完整性、机密性和资源可访问性方面发挥着重要作用。包的真实性分类是目前研究工作的主要对象，软计算已应用于包的真实性分类。如果数据集中的特征数量减少，软计算的复杂度就会大大降低。降维的管理和分析是所提出模型的新颖之处。不确定性的存在和入侵的不精确性似乎为这种结构创建了合适的模糊逻辑系统。神经模糊算法是将模糊逻辑系统融入自适应和分析能力的有效方法之一。在这项研究工作中，提出了软计算模糊逻辑系统，通过入侵检测来增强网络安全性。演示了三个网络数据集来测试和估计所提出的系统。特征选择用于从整个网络数据中删除不相关的特征，这些特征是障碍物分类过程。应用信息增益方法来选择检测入侵的重要特征。自适应神经模糊推理系统（ANFIS）进一步用于将分类网络数据的重要特征处理为正常或攻击数据包。基于 ANFIS 系统的两个函数命名为 Jang 的神经模糊和快速缩放共轭梯度 (SCG)。明显地，实验结果表明，该系统在检测正常或攻击方面取得了较高的精度。实验结果表明，与现有模型相比，所提出的系统结果在分类的准确性和时间过程上都有更好的表现。总体结果表明，所提出的系统能够有效地检测各种入侵。