Data Mining and Knowledge Engineering

R. James Marianathan ¹, A. Arul Lawrence Selvakumar ²

Affiliations
1 Electronics and Communication Engg., Karunya Institute of Technology, Karunya Nagar, Coimbatore, Tamil Nadu, IN
2 Systems and Marketing, Madurai Kamaraj University, Madurai, Tamil Nadu, IN

Abstract

As the quantity of information available increases exponentially and the general pace of life accelerates, the ability to navigate, access, validate and share information will be a pivotal skill in an increasingly complex environment. This skill will affect every part of a person’s existence, including everything from their ability to remain competitive in the workplace, to their ability to make personal choices about holidays, social activities and personal development projects.
As mobile technologies become a household name both student and educators, the questions about mobile learning are shifting from a common sense rationale of every student having a mobile device to whether mobile learning enhances or improve learning, and if so, at what and shoes cost. Proponents mobile learning argue that handheld devices enable learners to learn anywhere and anytime.
This article proposes four fundamental learning advantages that are gained more easily, or to a greater degree, through the use of mLearning than through traditional face to face instruction, paper based distance education or traditional eLearning. Based on secondary academic research, and field research into current examples of mLearning, this article provides an alternative definition for mobile learning (mLearning) and redicts some characteristics of the future learning environment, including the increased importance of metacognitive and literacy skills. It is proposed that mLearning can provide access, context, collaboration and appeal advantages and additional facilitation measures for facilitators. Finally, a glimpse of the future will be provided including hypothetical examples.

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G. Mohammed Nazer ¹, A. Arul Lawrence Selvakumar ²

Affiliations
1 PRIST University, IN
2 Department of Computer Applications, Adhiparasakthi College of Engineering, IN

Abstract

Intrusion detection system (IDSs) plays a vital role in the infrastructure protection mechanisms and these systems have to be accurate, adaptive and extensible. As the requirements and the complexities of today‟s network environment is becoming more and more, we need a more adaptive framework and automated IDS development process. This article describes a systematic data mining framework for constructing intrusion detection models. We propose to use the association rules and frequent episodes collected from audit data and to use these as basis for guiding the audit data gathering and feature selection processes. Our experiments on DARPA training audit data of network transmission activities showed that classification models can detect intrusions automatically in a more accurate way. We modify the two basic algorithms to use axis attribute(s) and variable attribute(s) to compute the relevant patterns. We use meta-learning as a mechanism to make IDs models more effective and adaptive. We report our experiment‟s results in using our framework on real-world audit data.

Keywords

Intrusion Detection, Classification, Audit Data, Association Rules, Frequent Episodes.

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