Open AccessAn Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset
Author(s) -
Sarthak Rastogi,
Archit Shrotriya,
Mitul Kumar Singh,
Raghu Vamsi Potukuchi
Publication year - 2022
Publication title -
journal of computing research and innovation
Language(s) - English
Resource type - Journals
ISSN - 2600-8793
DOI - 10.24191/jcrinn.v7i1.274
Subject(s) - intrusion detection system , computer science , decision tree , naive bayes classifier , support vector machine , random forest , machine learning , artificial intelligence , data mining , anomaly detection , statistical classification , anomaly based intrusion detection system , algorithm , pattern recognition (psychology)
From the past few years, Intrusion Detection Systems (IDS) are employed as a second line of defence and have shown to be a useful tool for enhancing security by detecting suspicious activity. Anomaly based intrusion detection is a type of intrusion detection system that identifies anomalies. Conventional IDS are less accurate in detecting anomalies because of the decision taking based on rules. The IDS with machine learning method improves the detection accuracy of the security attacks. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. The findings reveal which method performed better in terms of accuracy and running time.