A Survey on Intrusion Detection System using Machine Learning and Deep Learning

Authors(2) :-Hemavati, Dr. Aparna R

As we know internet of Things (IoT) is one of the fastest growing paradigm which is composed of Internet and different physical devices with different domains or the smart applications like home automation, business automation applications, health and environmental monitoring applications. The dependency on IOT devices is increasing day by day with our daily activities, which leads to most important challenge for security. Since having a better monitoring system for better security is a need. From more than two decades the concept or the frame work called IDS (Intrusion detection system) is playing important role for detecting the attacks in the network. Since the network attacks are not fixed in nature, a new type of attacks are happening on the network applications. There are many traditional IDS techniques are available but they are complex to apply. Since machine learning is one of the important area which is achieving good results in many applications. In this paper we study about the different machine learning techniques used till now and the methodology for the attack detection and the validation strategy. We will also discuss about the performance metrics.

Authors and Affiliations

Department of Information Science and Technology, SIT, Tumakuru, Karnataka, India
Dr. Aparna R
Department of Information Science and Technology, SIT, Tumakuru, Karnataka, India

IOT, IDS, deep learning, machine learning.

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 264-270
Manuscript Number : CSEIT195264
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Hemavati, Dr. Aparna R, "A Survey on Intrusion Detection System using Machine Learning and Deep Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.264-270, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195264
Journal URL : http://ijsrcseit.com/CSEIT195264

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