International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Downloads: 4 | Views: 20

Student Project | Information Technology | India | Volume 10 Issue 12, December 2021

Intrusion Detection using Machine Learning Techniques

Akshay Kaushik | Varun Goel

Abstract: An Intrusion is an uncredited access to a computer in your organization or a personal computer. As the world is becoming more internet-oriented and data leaks occur more than ever in our tech-savvy world, we need to know about these attacks so that they can be prevented hence coming into action Intrusion Detection System. IDS are systems that alert about the attack by analyzing the traffic on the network for signs of unauthorized activity. To identify the attack and alert about that possible attack, this system needs to be trained on some previous attacks data, for this study, the improved version of the KDD99 dataset, NSL-KDD dataset have been used for training the Machine Learning Model. In this analysis of Machine Learning algorithms, the algorithms under consideration are Logistic Regression, Support Vector Machine, Decision Tree, Random Forest. For comparison of the performance of the algorithms metrics like Accuracy Score, Confusion Matrix, and Classification Report were considered to find the best algorithm among them.

Keywords: Machine Learning, Intrusion Detection, Algorithm, Dataset, NSL-KDD, Attacks

Edition: Volume 10 Issue 12, December 2021,

Pages: 695 - 698

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How to Cite this Article?

Akshay Kaushik, Varun Goel, "Intrusion Detection using Machine Learning Techniques", International Journal of Science and Research (IJSR), Volume 10 Issue 12, December 2021, pp. 695-698,

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