A novel network security algorithm based on improved support vector machine from smart city perspective.

Computer generated security concerns have become more modern and complex. Intrusion detection(ID) is a practical issue in the field of computer security whose primary objective is to detect rare attack or assaults and to ensure the security of interior systems. This paper also proposes a semi-class intrusion detection method that combines multiple classifiers to arrange exceptions and typical exercises in a computer system. The abuse detection model is constructed in the light of the decision tree learning-iterative dichotomise 3(DTL-ID3) and is assembled by utilizing the gathered data based on anomaly detection model executed by one class-support vector machine(OC-SVM). In recent years, people have paid more attention to ID/intrusion prevention system (IDS / IPS), which is closely related to the protection and utilization of system management. A few machine-learning standards including neural system, direct hereditary programming, and advanced support vector machines(ASVMs), Bayesian system, multivariate versatile relapse splines, fluffy derivation systems(FIS) and other analogical systems have been researched for the outline of intrusion detection system. In this paper, we build up an amalgam method based on DTL-ID3 and OC-SVM(A-DT and SVM) and evaluate the performance of the projected methodology by using a specific dataset and a crossover method in order to enhance the accuracy of IDS/IPS when contrasted with a singular support vector machine.