Data Mining Methods for Attacks Recognition & Prevention

Authors

  • Shalini  Asst. Prof., Dept. of Computer Science & Engineering, Jyoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India

Keywords:

Intrusion, Intrusion Detection System, Anomaly & Misuse Detection

Abstract

In modern era, due to several benefits digital system and services has gain high attention in each and every field, especially in communication area. Amount of users are increasing day by day. However, this technique facilitates its user in number of ways within effective time period but with the growth of users and chip technology some fears has also comes in front of its users. Security and trustworthy information is one of the most issues with the use of such technique. Different novel approaches made an endeavor to to impacting availability, confidentiality, and integrity of critical data that poses a serious problem for their detection and exploits safety vulnerabilities. On the other hand human labeling of the available network audit data instances is usually tedious, time consuming and expensive. Therefore it is essential for a system administrator that he/she use one or more security tools to protect information from passing before curious eyes or, more importantly, from falling into wrong hands. This paper will examine the intrusion detection systems, one of the relative new technologies in information security. It aims to explore, in high level, the intrusion detection systems available today, as well as new developments in this area by using data mining methodologies. Apart of simple reviewing of accessible technique this study has also focus on current research issues of this field.

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International Journal of Scientific Research in Science and Technology

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Published

2017-08-31

Issue

Volume 3, Issue 6, July-August-2017

Section

Research Articles

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How to Cite

[1]
Shalini, " Data Mining Methods for Attacks Recognition & Prevention, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 3, Issue 6, pp.578-584, July-August-2017.