Network Intrusion Detection System Using Machine Learning
DOI:
https://doi.org/10.32628/CSEIT22819Keywords:
Support Vector Machine (SVM), Naive bayes, Dynamic Complex types of security.Abstract
The "Network Intrusion Detection System Based on Machine Learning Algorithms" is a component of software that invigilate a network of computers detecting potentially hazardous activities like capturing sensitive secret data or corrupting/hacking network protocols. Today's IDS techniques are incapable of doing this cope with the many sorts of security cyber-attacks on computer networks that are dynamic and complex. The effectiveness of an intruder the precision of detection is crucial. Intrusion detection accuracy must be able to reduce the number of false alarms and raise the pace at which alerts are detected. Various methods have been used to escalate the performance. In recent studies, approaches have been applied. The main function of this group is to analyze large amounts of network traffic data system for detecting intrusions to address this, a well-organized categorization system is necessary issue. Machine Learning methods like Support Vector Machine (SVM) and Na?ve bayes are applied for evaluation of IDS. NSL-KDD knowledge discovery data set is used, their accuracy and misclassification rate get calculated.
References
- Shetty Akshada, Jadhav Shailaja et. al., “Detection of fake accounts in online social networks (OSN)” International Journal of Modern Trends in Engineering and Science- IJMTES 2017, Volume 4 -Issue 5 Pages 1-3.
- Shailaja Jadhav, Minal Pokale, “Detecting attacks for security of information using Data Mining Technique” IJMTES 2017, Volume 4 Issue 05C. Chang and C. J. Lin, LIBSVM, “A Library for Support Vector Machines”, the use of LIBSVM, 2009.
- Rung-Ching Chen, Kai-Fan Cheng and Chia-Fen Hsieh, “Using Rough Set and Support Vector Machine for Network Intrusion Detection”, International Journal of Network Security & Its Applications (IJNSA), Vol 1, No 1, 2009.
- Phurivit Sangkatsanee, Naruemon Wattanapongsakorn and Chalermpol Charnsripinyo, “Real-time Intrusion Detection and Classification”, IEEE network, 2009.
- Liberios Vokorokos, Alzebeta Kleniova, “Network Security on the Intrusion Detection System Level”, IEEE network, 2004.
- Thomas Heyman, Bart De Win, Christophe Huygens, and Wouter Joosen, “Improving Intrusion Detection through Alert Verification”, IEEE Transaction on Dependable and Secure Computing, 2004.
- T. Lin and C.J. Lin, “A study on sigmoid kernels for SVM and the training of non- PSD kernels by SMO-type methods”, Technical report, Department of Computer Science, National Taiwan University, 2003.
- Jagdish Jangid , " Efficient Training Data Caching for Deep Learning in Edge Computing Networks" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 5, pp.337-362, September-October-2020. Available at doi : https://doi.org/10.32628/CSEIT20631113
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.