Streamlit Based Network Intrusion Detection System Prototype with Machine Learning Algorithm - Tiara Maulida; Muhammad Nandi Buchari; Teofilus Tirta Jumata; Putra Pratama Syahrival; Ali Mustopa (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
Computer network security has become a crucial elemen in the digital era, with the increasing risk of attacks that could potentially disrupt systems and access critical data. An Intrusion Detection System (IDS) powered by Machine Learning is one effective way to automatically detect suspicious network activity. This study aims to create a prototype of a network Intrusion Detection System using Streamlit that applies Machine Learning algorithms, including Naïve Bayes and Random Forest, to classify normal network activity as an attack. The method used in this study is a quantitative approach with an experimental design utilizing a public dataset of labeled network traffic. The research process includes the stages of initial data processing, feature selection, model creation, performance evaluation, and implementation of the Streamlit interface. Test results show that the Naïve Bayes algorithm has the best performance, with an accuracy level reaching 0.8000, an error rate of 0.2000, and an F1 Score of 0.7273. Random Forest recorded an accuracy level of 0.7333, an error rate of 0.2667, and a lower F1 Score of 0.3333. These findings demonstrate that Naïve Bayes is more effective at detecting intrusions and recognizing anomalous network traffic patterns. The Streamlit based system implementation successfully provides an interactive and userfriendly interface, allowing users to perform analysis and understand classification result without in-depth technical expertise. Given the foregoing, the network intrusion detection system prototype built with Streamlit and a Machine Learning algorithm is considered suitable as a simple, informative, interactive, and efficient network security support tool. This research paves the way for future developments, such as the implementation of Deep Learning models and the integration of live network monitoring.