Version 1
: Received: 7 July 2020 / Approved: 9 July 2020 / Online: 9 July 2020 (12:23:29 CEST)
How to cite:
Bandyopadhyay, S.; Chowdhury, R.; Banerjee, P.; Dey, S.D.; Saha, B. A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks. Preprints2020, 2020070191. https://doi.org/10.20944/preprints202007.0191.v1
Bandyopadhyay, S.; Chowdhury, R.; Banerjee, P.; Dey, S.D.; Saha, B. A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks. Preprints 2020, 2020070191. https://doi.org/10.20944/preprints202007.0191.v1
Bandyopadhyay, S.; Chowdhury, R.; Banerjee, P.; Dey, S.D.; Saha, B. A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks. Preprints2020, 2020070191. https://doi.org/10.20944/preprints202007.0191.v1
APA Style
Bandyopadhyay, S., Chowdhury, R., Banerjee, P., Dey, S.D., & Saha, B. (2020). <strong>A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks</strong>. Preprints. https://doi.org/10.20944/preprints202007.0191.v1
Chicago/Turabian Style
Bandyopadhyay, S., Soumya Deep Dey and Banani Saha. 2020 "<strong>A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks</strong>" Preprints. https://doi.org/10.20944/preprints202007.0191.v1
Abstract
. In today’s world, cyber attack is one of the major issues concerning the organizations that deal with technologies like cloud computing, big data, IoT etc. In the area of cyber security, intrusion detection system (IDS) plays a crucial role to identify suspicious activities in the network traffic. Over the past few years, a lot of research has been done in this area but in the current scenario, network attacks are diversifying in both volume and variety. In this regard, this research article proposes a novel IDS where a combination of information gain and decision tree algorithm has been used for the purpose of dimension reduction and classification. For experimental purpose the NSL-KDD dataset has been used. Initially out of 41 features present in the dataset only 5 high information gain valued features are selected for classification purpose. The applicability of the selected features are evaluated through various machine learning based algorithms. The experimental result shows that the decision tree based algorithm records highest recognition accuracy among all the classifiers. Based on the initial classification result a novel methodology based on decision tree has been further developed which is capable of identifying multiple attacks by analyzing the packets of various transactions in real time.
Keywords
Intrusion Detection System; NSL-KDD Dataset; One Hot Encoding; Information Gain; Decision Tree
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
