Open Access   Article Go Back

Review of Hybrid Intrusion Detection System

S. Soni1 , P. Sharma2

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 1100-1104, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.11001104

Online published on Jun 30, 2018

Copyright © S. Soni, P. Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: S. Soni, P. Sharma, “Review of Hybrid Intrusion Detection System,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1100-1104, 2018.

MLA Style Citation: S. Soni, P. Sharma "Review of Hybrid Intrusion Detection System." International Journal of Computer Sciences and Engineering 6.6 (2018): 1100-1104.

APA Style Citation: S. Soni, P. Sharma, (2018). Review of Hybrid Intrusion Detection System. International Journal of Computer Sciences and Engineering, 6(6), 1100-1104.

BibTex Style Citation:
@article{Soni_2018,
author = {S. Soni, P. Sharma},
title = {Review of Hybrid Intrusion Detection System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {6},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {1100-1104},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2306},
doi = {https://doi.org/10.26438/ijcse/v6i6.11001104}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i6.11001104}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2306
TI - Review of Hybrid Intrusion Detection System
T2 - International Journal of Computer Sciences and Engineering
AU - S. Soni, P. Sharma
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 1100-1104
IS - 6
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
371 197 downloads 117 downloads
  
  
           

Abstract

Insurance of computer assets and put away archives is a vital issue in this day and age. Intruders have made numerous triumphant endeavors to topple esteemed organization systems. In spite of the fact that the present security arrangements, for example, firewalls and hostile to infection programming have their critical parts in securing associations however they don`t identify a wide range of attacks of the present digital world. Intrusion detection is a system used to identify different attacks on a system. There are numerous Intrusion detection Systems (IDSs) accessible today. This paper gives a brief introduction about intrusion detection system and its components. Further, classification of intrusion detection system is discussed. Also various researches done in previous years are discussed.

Key-Words / Index Term

intrusion detection system, data mining, confidentiality, integrity, availability, intrusion detector.

References

[1] Sanjay Sharma, R. K. Gupta, “Intrusion Detection System: A Review”, International Journal of Security and Its Applications, Vol. 9, No. 5, pp. 69-76, 2015.
[2] Sheenam, Sanjeev Dhiman, “Comprehensive Review: Intrusion Detection System and Techniques”, IOSR Journal of Computer Engineering, Vol. 18, Issue. 4, pp. 20-25, 2016.
[3] Rajni Tewatia, Asha Mishra, “Introduction To Intrusion Detection System: Review”, International Journal of Scientific & Technology Research, Vol. 4, Issue. 5, pp. 219-223, 2015.
[4] D. Ashok Kumar, S. R. Venugopalan, “Intrusion Detection Systems: A Review”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 8, pp. 356-370, 2017.
[5] Kajal Rai, M. Shyamala Devi, “Intrusion Detection Systems: A Review”, Journal of Network and Information Security, Vol. 1, Issue. 2, pp. 15-21, 2013.
[6] Shadi Aljawarneh, Muneer Bani Yassein, Mohammed Aljundi, “An enhanced J48 classification algorithm for the anomaly intrusion detection systems”, Springer, 2017.
[7] Varsha Singh, Shubha Puthran, Avanish Tiwari, “Intrusion Detection Using Data Mining with Correlation”, IEEE, International Conference for Convergence in Technology, pp. 620-625, 2017.
[8] Sandeep Kaur, Dr. Sheetal Kalra, “Disease Prediction using Hybrid K-means and Support Vector Machine”, IEEE, 2016.
[9] Roshan Chitrakar, Huang Chuanhe, “Anomaly based Intrusion Detection using Hybrid Learning Approach of combining k-Medoids Clustering and Naïve Bayes Classification”, IEEE, 2012.
[10] Manish Kumar, Dr. M. Hanumanthappa, Dr. T. V. Suresh Kumar, “Intrusion Detection System Using Decision Tree Algorithm”, IEEE, pp. 629-634, 2012.
[11] Imtiaz Ullah, Qusay H. Mahmoud, “A Filter-based Feature Selection Model for Anomaly-based Intrusion Detection Systems”, IEEE, International Conference on Big Data, pp. 2151-2159, 2017.
[12] Luigi Coppolino, Salvatore D’Antonio, Alessia Garofalo, Luigi Romano, “Applying Data Mining Techniques to Intrusion Detection in Wireless Sensor Networks”, IEEE, International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 247-254, 2013.
[13] Saad Mohamed Ali Mohamed Gadal, Rania A. Mokhtar, “Anomaly Detection Approach using Hybrid Algorithm of Data Mining Technique”, IEEE, International Conference on Communication, Control, Computing and Electronics Engineering, 2017.
[14] Jithin Mathew, S. Ajikumar, "Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Vol. 2, Issue. 2, pp.92-97, March-April.2017.
[15] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, Int. J. Sci. Res. in Network Security and Communication, Vol. 5, Issue. 6, pp. 5-8, Dec 2017.
Authors Profile