ABSTRACT

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Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. An Intrusion Detection is an important in assuring security of network and its different resourses. Intrusion detection attempts to detect computer attacks by examining various data records observed in processes on the network. Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection. Intrusion detection systems aim to identify attacks with a high detection rate and a low false positive. Here, we are going to propose Intrusion Detection System using data mining technique: Support Vector Machine (SVM). Support vector machine-based intrusion detection methods are increasingly being researched because it can detect novel attacks. But solving a support vector machine problem is a typical quadratic optimization problem, which is influenced by the feature dimensions and number of training samples. In this paper how the support vector machines are used for intrusion detection are described and finally proposed a solution to the inrusion detection system.

Edge detection is one of the most commonly used operations in image processing and pattern recognition. Edge detecting in an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. In this paper, edge detection methods such as Sobel, Prewitt, Robert, Canny, Laplacian of Gaussian (LOG), Expectation-Maximization (EM) algorithm, OSTU and Genetic algorithms are also used for segmenting. A new edge detection technique is proposed which detects the sharp and accurate edges that are not possible with the existing techniques. This implemented edge detection technique will be improved by combining it with other types of filters namely Weiner, STD, Hormonic, Geometric filters to remove the noise from the image. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results with different threshold values for given input image which ranges between 0 and 1. When the threshold value is 0.68 it is noticed that the sharp and accurate edges are detected.