An Intelligent Approach to Network Intrusion Detection using Machine Learning
Abstract
Keeping networks secure is a big challenge for today’s organizations, especially with cyber threats growing more sophisticated all the time. Traditional signature-based intrusion detection systems aren’t enough anymore—they rely on known patterns, so they often miss new or complex attacks. This study introduces a machine learning-based intrusion detection system that spots threats using data-driven analysis instead of just matching signatures. The system sorts network traffic into safe or malicious categories by using multiple machine learning models, like Random Forest, Support Vector Machine, Decision Tree, and K-Nearest Neighbor. These models were trained on well-known benchmarking datasets (CICIDS 2017 and CICIDS 2019), which gives them a solid foundation. To improve accuracy and cut down on false alarms, the system goes through thorough data preparation, careful feature selection, and fine-tuning during training. Plus, the Streamlit platform adds interactive visualizations and motion, making it easier to explore and understand what’s happening across the network.References
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