Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{202511,
        author = {Prof. Minal Rahul Chaudhari},
        title = {AI-Driven Network Intrusion Detection Using Weka},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7340-7342},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202511},
        abstract = {With the rise in sophisticated cyber-attacks, traditional security measures are no longer sufficient. This study employs Artificial Intelligence (AI) to detect anomalies in network traffic. Using the Waikato Environment for Knowledge Analysis (Weka) tool, we compare the performance of three machine learning classification algorithms—J48 (Decision Tree), Random Forest, and Naïve Bayes—to detect intrusions. Our methodology focuses on data preprocessing, feature selection, and classification modeling using a preprocessed KDD Cup dataset to detect malicious traffic, with results indicating that the Random Forest algorithm provides the highest accuracy.},
        keywords = {Cyber Security, Machine Learning, Artificial Intelligence, Classification Model.},
        month = {May},
        }