REVIEW THE INTRUSION DETECTION SYSTEM (IDS) TECHNIQUE USING MACHINE LEARNING
Author Name: 1. Prof Khushbu Rai, 2. Ms. Geeta Das
Volume/Issue: 01/12
Country: India
DOI NO.: 08.2020-25662434 DOI Link: http://www.doi-ds.org/doilink/05.2021-85727481/UIJIR
Affiliation:
- Professor Of Lakshmi Narain College of Technology & Science, Madhya Pradesh, India
- Student, Lakshmi Narain College of Technology & Science, Madhya Pradesh, India
ABSTRACT
Machine learning techniques are widely used in the development of intrusion detection systems (IDS) capable of identifying and distinguishing cyberattacks at the network and host levels in a timely and automatic manner. However, since malicious attacks are continually emerging and occurring in large numbers, some issues arise that necessitate a modular solution. Various ransomware datasets are publicly available for the computer security community to review more. However, no recent study has investigated the performance of various machine learning algorithms on a number of publicly available datasets. This survey proposes an IDS taxonomy that categorises and summarises IDS literature focused on machine learning and deep learning, with data objects serving as the primary component. We both accept that this kind of taxonomy scheme is suitable for computer security researchers. The survey first clarifies the description and taxonomy of IDSs. Then, the machine learning techniques, metrics, and benchmark datasets that are often used in intrusion detection systems are addressed.
Key words: Intrusion detection system(IDS), Support Vector Machine(SVM)
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