Intrusion detection system (IDS) is the system whichidentifies malicious activity on the network. As the Internet volume is increasing rapidly, security against the real time attacks and their fast detection issues gain attention of many researchers. Data mining methods can be effectively applied to (IDS) to tackle the problems of dynamic huge network data and to improve IDS performance. We can reduce the time complexity by selecting only useful features to build model for classification. There are many features selection techniques are developed either to select the features or extract features. In this paper, an evolutionary approach for feature selection is proposed which is based on mathematical intersection principle. Genetic algorithm (GA) is used as a search method while selecting features from full NSL KDD data set along with the intersection principle of selecting those only who appears everywhere in the experiment. The results of proposed approach when compared using classifiers, it shows tremendous growth in accuracy of a Naïve Bayes classifier with reduced time and minimum number of features.
Recent emerging growth of data created so many challenges in data mining. Data mining is the process of extracting valid, previously known & comprehensive datasets for the future decision making. As the improved technology by World Wide Web the streaming data come into picture with its challenges. The data which change with time & update its value is known as streaming data. As the most of the data is streaming in nature, there are so many challenges need to face in the sense of security perspective. Intrusion Detection System (IDS) works in the supposition of detecting the intruders to protect the respective system. The research in data stream mining & Intrusion detection system gained high attraction due to the importance of system's safety measure. Algorithms, systems & frameworks that address security challenges have been developed over the past years. In this paper, we present the mechanism to improve the efficiency of the IDS using streaming data mining technique. We apply four selected stream data classification algorithms on NSL-KDD datasets and compare their results. Based on the comparative analysis of their results best method is found out for efficiency improvement of IDS.
Multiple object tracking is being used for many applications nowdays such as automated surveillance, Robotics,self driving cars,medical and many more. There have been continuous improvements in existing state of art MOT(multiple object tracking) methods through many methods and global optimization techniques.This paper focuses on various MOT techniques and how to achieve speedup and efficiency using MOT methods.
In today's world data is rapidly and continuously growing and is not constant in nature. There is a problem to deal with such kind of evolving data, as it is impractical to store and process this streaming data. Also, in real world application, the data which is coming is typically noisy, has some missing values, redundant features, and thus very large time is wasted to preprocess that data. The time complexity can reduce by selecting only useful features to build model for classification. The proposed system addresses the issue of adaptive preprocessing for streaming data. Here Genetic algorithm (GA) is used as a search method while selecting the features which will further use in learning model. The proposed system is applied to different stream datasets and is showing significant increment in classification accuracy.
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