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Reseach Article

A Novel Survey on Intrusion Detection using Data Mining

Published on February 2015 by Purtata Bhoir, Shilpa Kolte
International Conference on Advances in Science and Technology
Foundation of Computer Science USA
ICAST2014 - Number 4
February 2015
Authors: Purtata Bhoir, Shilpa Kolte
a99879f9-ccb3-4ca4-af37-4ed698d842a9

Purtata Bhoir, Shilpa Kolte . A Novel Survey on Intrusion Detection using Data Mining. International Conference on Advances in Science and Technology. ICAST2014, 4 (February 2015), 26-29.

@article{
author = { Purtata Bhoir, Shilpa Kolte },
title = { A Novel Survey on Intrusion Detection using Data Mining },
journal = { International Conference on Advances in Science and Technology },
issue_date = { February 2015 },
volume = { ICAST2014 },
number = { 4 },
month = { February },
year = { 2015 },
issn = 0975-8887,
pages = { 26-29 },
numpages = 4,
url = { /proceedings/icast2014/number4/19496-5048/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Science and Technology
%A Purtata Bhoir
%A Shilpa Kolte
%T A Novel Survey on Intrusion Detection using Data Mining
%J International Conference on Advances in Science and Technology
%@ 0975-8887
%V ICAST2014
%N 4
%P 26-29
%D 2015
%I International Journal of Computer Applications
Abstract

Database security is vital nowadays as database system contain valuable information. In today's computer world, attacks are used to disclose, destroy, alter, or steal information. The information security plays vital role to protect confidentiality, integrity and availability of information. Intrusion detection system (IDS) is one of the important components of strong information security system. IDS serve three security functions: they monitor, detect and respond to unauthorized activity. Researchers are working on various data mining techniques such as access patterns of users, data dependencies to detect malicious attacks. Data mining is widely used to find useful patterns from large volume of data. In this paper we have enlisted some existing ID approaches of data mining for detecting insider attacks and compared them with considering their advantages and disadvantages.

References
  1. Mohammad M. , Javidi Mina Sohrabi and Marjan Kuchaki Rafsanjani: Intusion detection in databasesystem, Springer-Verlag Berlin Heidelberg 2010.
  2. Ashish Kamra, Evimaria Terzi, and Elisa Bertino: Detecting Anomalous Access Patterns in Relational Database.
  3. Mohammad M. , Javidi Mina Sohrabi and Marjan Kuchaki Rafsanjani: An overview of anomaly based database intrusion detection system, Indian Journal of Science and Technology (2012).
  4. Dr. M. Amrutha, Prabhakar, M. KarthiKeyan, Prof. K. Marimuthu, An Efficient technique for preventing SQL injection attack using pattern matching algorithm,IEEE(2013) .
  5. William G. J. Halfond, Alessandro Orso, and Panagiotis Manolios, Using Positive Tainting and Syntax –Aware Evaluation to Counter SQL Injection Attack.
  6. Cristina Yip Chung, Michael Gertz, Karl Levitt, DEMIDS: A misuse Detection system for Database System, Springer 2000.
  7. Sudam Kokane,Aishwarya Jadhav,Nikita Mandhare,Mayur Darekar,Intrusion detection in RBAC Model,International Journal of Innovative Research & Studies May(2013).
  8. Abhinav Srivastava, Shamik Sural and A. K. Majumdar: Database Intrusion Detection using Weighted Sequence Mining, Journal of Computers, Vol. 1 (2006).
  9. R. Agrawal, R. Srikant: Mining Sequential Patterns, International Conference Data Engineering (1995).
  10. Yi Hu, Alina Campan, James Walden, Irina Vorobyeva, Justin Shelton: An Effective Log Mining for Database Intrusion Detection, IEEE (2010).
  11. W. Wang, J. Yang, P. S. Yu: Efficient Mining of Weighted Association Rules, ACM SIGKDD Conference on Knowledge Discovery and Data mining (2000).
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection Security Rbac Data Dependency Weighted Sequence Mining Data Mining.