Combined analysis of support vector machine and principle component analysis for IDS

摘要

In the modern world we are using the smart devices for storing data, retrieving data and processing the data on the cloud which is energy users to manage a wide variety of subscribers, reading devices for measuring, billing, disconnection and connection of subscribers from the connection management is an important issue. The performance of these intelligent systems is based on information transfer in the context of storing Big data, so reported data from network should be managed to avoid the malicious activities that including the issues that could affect the quality of service the system. In this paper for control of the reported wireless data and to ensure the veracity of the obtained information, using intrusion detection system is proposed based on the support vector machine and principle component analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done on five different kernels for an intrusion detection system using support vector machine with PCA simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in terms of time - response, rate of increase network efficiency and increase system error and differences in the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM algorithm reduces the time for data analysis and enhances performance of intrusion detection.