Network Intrusion Detection System Using Decision Tree

Year : 2024 | Volume :12 | Issue : 02 | Page : –
By

Ashwini Garole

Yash Jadhav

Aman Patil

Harsh Deshpande

Komal Sawant

  1. Assistant Professor Computer science and engineering, Vishwaniket’s iMEET, Khalapur Maharashtra India
  2. Student Computer science and engineering (Artificial Intelligence and Machine Learning) Vishwaniketan’s iMEET Khalapur Maharashtra India
  3. Student Computer science and engineering (Artificial Intelligence and Machine Learning) Vishwaniketan’s iMEET Khalapur Maharashtra India
  4. Student Computer science and engineering (Artificial Intelligence and Machine Learning) Vishwaniketan’s iMEET Khalapur Maharashtra India
  5. Student Computer science and engineering (Artificial Intelligence and Machine Learning) Vishwaniketan’s iMEET Khalapur Maharashtra India

Abstract

This paper presents a novel approach to Network Intrusion Detection Systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making processes, and efficiency in real-time threat detection. The system architecture includes key components such as a data collection module for capturing network traffic, a preprocessing module for data cleansing and feature extraction, and a decision tree classifier for classifying traffic into benign and malicious categories. The classifier’s performance is rigorously evaluated using metrics like accuracy, precision, recall, and F1-score, demonstrating superior performance with a 100% accuracy rate in model evaluation. The IDS’s effectiveness is compared against other machine learning techniques like K-Nearest Neighbors, Logistic Regression, and Naive Bayes, with decision trees showing the highest accuracy and efficiency. The paper also highlights future directions, including enhanced machine learning integration, behavioral analysis, cloud-based deployment, IoT security monitoring, and integration with threat hunting and incident response tools. This research underscores the potential of decision tree-based NIDS in providing robust, scalable, and comprehensible intrusion detection, crucial for protecting large-scale, dynamic network environments from diverse cyber threats.”

Keywords: Machine Learning Algorithms, Deep Learning, Classification Techniques, Decision, Tree, Logistic Regression, K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), Supervised Learning, Anomaly Detection, Support Vector Machine, Feature Selection, Data Preprocessing, Accuracy, Precision, Real-Time Intrusion Detection.

[This article belongs to Journal Of Network security(jons)]

How to cite this article: Ashwini Garole, Yash Jadhav, Aman Patil, Harsh Deshpande, Komal Sawant. Network Intrusion Detection System Using Decision Tree. Journal Of Network security. 2024; 12(02):-.
How to cite this URL: Ashwini Garole, Yash Jadhav, Aman Patil, Harsh Deshpande, Komal Sawant. Network Intrusion Detection System Using Decision Tree. Journal Of Network security. 2024; 12(02):-. Available from: https://journals.stmjournals.com/jons/article=2024/view=152713

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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received May 21, 2024
Accepted June 23, 2024
Published July 2, 2024
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