Open AccessIntrusion Detection System using Machine Learning
Author(s) -
Jayesh Zala,
Aditya Panchal,
Advait Thakkar,
Bhagirath Prajapati,
Priyanka Puvar
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2062166
Subject(s) - intrusion detection system , computer science , key (lock) , preprocessor , anomaly based intrusion detection system , machine learning , software , computer security , data pre processing , intrusion prevention system , artificial intelligence , network security , data mining , operating system
Intrusion Detection System (IDS) is a tool, or software application, that monitors network or system activity and detects malicious activity occurring. The protected evolution of the network must incorporate new threats and related approaches to avoid these threats. The key role of the IDS is to secure resources against the attacks. Several approaches, methods and algorithms of the intrusion detection help to detect a plethora of attacks. The main objective of this paper is to provide a complete system to detect intruding attacks using the Machine Learning technique which identifies the unknown attacks using the past information gained from the known attacks. The paper explains preprocessing techniques, model comparisons for training as well as testing, and evaluation technique.