Author(s):
Shabnam Chandrakar, Harsha Verma, Rubi Kambo
Email(s):
shabnamchandrakar@gmail.com , vermaharsha795@gmail.com , Rubi.kambo7@gmail.com
DOI:
10.5958/2321-581X.2020.00019.7 
Address:
Shabnam Chandrakar1, Harsha Verma2, Mrs. Rubi Kambo3
1,2Student, SoS in Computer Science & IT, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India.
3Assistant Professor, SoS in Computer Science & IT, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India.
*Corresponding Author
Published In:
Volume - 11,
Issue - 2,
Year - 2020
ABSTRACT:
Most of the emergence of IT, Now a day’s dependency over the web or network increases, Confidentiality, Integrity, and security of user data must be needed while exchanging of data. So as it may harm or attack by intruder for these Intrusion detection System (IDS) were developed earlier. In this paper, we try to find out or discover or study of Data Mining Techniques that were proposed earlier, will result in latter Intrusion Detection System.
Cite this article:
Shabnam Chandrakar, Harsha Verma, Rubi Kambo. A Study on Intrusion Detection System Using Datamining Techniques. Research J. Engineering and Tech. 2020;11(2):109-112. doi: 10.5958/2321-581X.2020.00019.7
Cite(Electronic):
Shabnam Chandrakar, Harsha Verma, Rubi Kambo. A Study on Intrusion Detection System Using Datamining Techniques. Research J. Engineering and Tech. 2020;11(2):109-112. doi: 10.5958/2321-581X.2020.00019.7 Available on: https://ijersonline.org/AbstractView.aspx?PID=2020-11-2-15
REFERENCES:
1. Niranjan A, Nitish A, P Deepa Shenoy & Venugopal K R; “Security in Data Mining- A Comprehensive Survey”; Global Journals Inc.(USA), Volume: 16 Issue 5 Year 2016, Online ISSN: 0975-4172 & Print ISSN: 0975-4350.
2. Mr. Mohit Tiwari, Raj Kumar, Akash Bharti, Jai kishan; “Intrusion Detection System”; International Journal of Technical Reaserch and Applications”; Volumn: 5, Electronic-ISSN:2320-8163, pp. 38-44, Issue 2 (March-April 2017).
3. Muamer N. Mohammada, Norrozila Sulaimana, Osama Abdulkarim Muhsin ; “A Novel Intrusion Detection System by using Intelligent Data Mining in Weka Environment”; Elsevier Ltd. Open access under CC BY-NC-ND license”; DOI:10.1016/j.procs.2010.12.198.
4. Manisha Kansra, Pankaj Dev Chadha; “Cluster Based detection of Attack IDS using Datamining”; International Journal of Engineering Development and Research”; Volumn: 4, Electronic-ISSN:2321-9939, Issue 3 (2016).
5. Wenke Lee and Salvatore J. Stolfo; “Data Mining Approaches for Intrusion Detection”; the 7th USENIX Security Symposium San Antonio, Texas”; 26-29 January 1998.
6. Ketan Sanjay Desale Chandrakant , Namdev Kumathekar, Arjun Pramod Chavan; “Efficient Intrusion Detection System using Stream Data Mining Classification Technique”; International Conference on Computing Communication Control and Automation, 2015.
7. Cheung-Leung Lui, Tak-Chung, Fu Ting-Yee Cheung; “Agent-based Network Intrusion Detection System Using Data Mining Approaches”; IEEE, Print ISBN: 0-7695-2316-1, 01 August 2005, DOI: 10.1109/ICITA.2005.57.
8. Fadwa Abdul Aziz Alseiari and Zeyar Aung; “Real-Time Anomaly-Based Distributed Intrusion Detection Systems for Advanced Metering Infrastructure Utilizing Stream Data Mining”; International Conference on Smart Grid and Clean Energy Technologies”; Volumn: 5, Electronic-ISSN:2320-8163, pp. 38-44, Issue 2 ( 2015).
9. Sheraz Naseer, Yasir Saleem, Shehzad Khalid, Muhammad Khawar Bashir, Jihun Han, Muhammad Munwar Iqbal, Kijun Han; “Enhanced Network Anomaly Detection Based on Deep Neural Networks”; IEEE, Volumn:6, Electronic-ISSN:2169-3536, 17Augustl 2018.
10. K. Raja, and M. Lilly Florence; “Tracking of Intruder in Local Area Network Using Decision Tree Learning Algorithms”; Asian Journal of Applied Sciences (ISSN: 2321 0893)Volume 05 – Issue 01, February 2017, DOI: 10.1109/Big Data Security-HPSC-IDS.2016.39.
11. Anna Little, Xenia Mountrouidou, Daniel Moseley; “Spectral Clustering Technique for Classifying Network Attacks”; IEEE, DOI: 10.1109/Big Data Security-HPSC-IDS.2016.39.