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Abdul Rauf, H.
- Classifier Selection Model Based on Gain Ratio Feature Selection Method
Authors
1 Department of Computer Science and Engineering, Avinashilingam University for Women, Coimbatore, Tamilnadu, IN
2 MEA Engineering College, Kerala, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 15 (2011), Pagination: 934-939Abstract
The computer networks usage has grown enormous and widespread, which has increased the number of new threats to a great extent. Intruder is one of the most publicized threats to security. In recent years, intrusion detection has emerged as an important technique for network security. Although there are some existing techniques for intrusion detection, there is a need to improve the performance. Data mining techniques have been applied as a new approach for intrusion detection. The quality of the feature selection methods is one of the important factors that affect the effectiveness of the Intrusion Detection system (IDS). In this paper, feature selection method, Gain Ratio is used to extract an optimal subset of features, which are then subjected to a set of classification algorithms to analyze KDDCup'99 dataset. We used 10-fold cross validation for building our proposed model. The classification algorithms are compared in terms of accuracy, detection rate, false alarm rate and time taken.Keywords
Intrusion Detection, Gain Ratio, KDDCup'99, Classification, Cross Validation.
- Big Data Hadoop:A Survey on Security Issues, Challenges and Solutions
Authors
1 Manonmaniam Sundaranar University, Tirunelveli, TN, IN
2 Sree Sastha Institute of Engineering and Technology, Chennai, TN, IN
3 St. Xavier’s College, Palayamkottai, TN, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 2 (2018), Pagination: 25-27Abstract
Big data is widening its popularity among data handlers. Hadoop has its own security measures as the top priority. But still it can be augmented with additional security features in the areas where we can identify threats and improvise its security features, so that authentic users can feel better safety on their data. Because data that come from heterogeneous sources have their importance felt at their originating point, under idealistic circumstances, we expect full privacy and security will be ascertained on our data while it is being stored and maintained in common storage. In order to provide such a kind of satisfaction, in this paper we try to identify the areas where security can further be enhanced. Also, we discuss with the threats or challenges that may be imposed in this move. For such situations we suggest remedial counter actions to get out of them.
Keywords
Big Data, Security, Privacy, Hadoop, Map Reduce.- Overview of Big Data Analytics System for Storing and Processing Huge Data
Authors
1 Manonmaniam Sundaranar University, Tirunelveli, TN, IN
2 Sree Sastha Institute of Engineering and Technology, Chennai, TN, IN
3 St. Xavier’s College, Palayamkottai, TN, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 2 (2018), Pagination: 28-30Abstract
Big data uses storage of huge data with some approaches and techniques to manage and process them. During the past few years the number of persons using internet, email and other internet based applications have been growing tremendously. Big Data is mainly characterized by Volume, Velocity and, Variety. The Big Data Architecture Framework (BDAF) is proposed to address all aspects of the Big Data Ecosystem and includes the following components: Big Data Infrastructure, Big Data Analytics, Data structures and models, Big Data Lifecycle Management, Big Data Security. The volume of data used is increasing exponentially. So, the need for storing, processing and protecting large volume of data has been becoming a great challenge in the modern hyper-connected world. Thousands of software professionals and others are doing their jobs with their internet connected laptops and mobile phones on the basis of work from home concept for development, implementation, testing and maintenance of various applications. These professionals and experts are sending and receiving lot of data to their clients, higher authorities and other officials on daily or weekly or other requirement basis. The traditional data management models are not efficient in Big data analytics. In this paper we try to give an overview of Big Data Analytics system for storing and processing huge data.