An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset

Sarthak Rastogi; Archit Shrotriya; Mitul Kumar Singh; Raghu Vamsi Potukuchi

doi:10.24191/jcrinn.v7i1.274

An Analysis of Intrusion Detection Classification using Supervised Machine Learning Algorithms on NSL-KDD Dataset

Authors

Sarthak Rastogi Department of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, India
Archit Shrotriya Department of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, India
Mitul Kumar Singh Department of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, India
Raghu Vamsi Potukuchi Department of CSE, Jaypee Institute of Information Technology, Sector 62, NOIDA, India

DOI:

https://doi.org/10.24191/jcrinn.v7i1.274

Keywords:

NSL-KDD, Intrusion Detection System, Machine Learning, Anomaly, SVM, KNN, Logistic Regression

Abstract

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.

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