Paper Title :Network Intrusion Detection System Using Machine Learning
Author :Ch. Naga Nikhila, S. Thasleem, P.V. Bhanusri, Kayalvizhi S
Article Citation :Ch. Naga Nikhila ,S. Thasleem ,P.V. Bhanusri ,Kayalvizhi S ,
(2024 ) " Network Intrusion Detection System Using Machine Learning " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 134-138,
Volume-12,Issue-6
Abstract : Network intrusion detection systems, or NIDS, are essential for defending computer networks from several types
of security threats and assaults. There is a growing need within networkattacks become more complicated and frequent.
sophisticated analytics methods to improve NIDS's capacity for detection and reaction. The creation and application of
analytics techniques for network intrusion detection systems is the main topic of this study. Utilizing these methods can help
NIDS be more accurate, efficient, and effective at detecting and mitigating security breaches. The first part of the study looks
at the foundational ideas of network intrusion detection, including the various kinds of attacks and the difficulties in
detecting them. The advantages of several NIDS systems, such as signature-based and anomaly-based systems, are
highlighted and restricted. The overall goal of this research is to enhance the field of network intrusion detection by using
analytics approaches to increase NIDS's capabilities. The suggested techniques can decrease false positives, enhance
automated incident response, process large amounts of data more effectively, and increase the accuracy of attack detection.
In the face of constantly changing cyber threats, the research findings will aid in the creation of stronger and more efficient
network security solutions.
Keywords - Network intrusion detection systems, Analytics techniques, Security breaches, False positives, Cyber threats
Type : Research paper
Published : Volume-12,Issue-6
Copyright: © Institute of Research and Journals
| |
 |
| |
 |
PDF |
| |
Viewed - 20 |
| |
Published on 2024-10-30 |
|
