Real-Time DDoS Attack Detection using Random Forest and Streamlit: A Machine Learning-Based Web Solution
Abstract
DDoS assaults threaten network availability and security in today's linked world. We propose a real-time DDoS assault detection method using machine learning, specifically the Random Forest algorithm. Our technology fits smoothly into web settings using the renowned Streamlit architecture, giving an easy and interactive platform for threat monitoring and mitigation. We collect a large dataset of network traffic features from benign and harmful operations. After careful preprocessing and feature engineering, we prepare data for training and evaluation. We use the resilient and scalable Random Forest method to create a predictive model that can distinguish typical traffic patterns from DDoS attacks. The model is rigorously tested using performance indicators to detect and categorize DDoS assaults with low false positives. The model is effortlessly integrated into a Streamlit-based online application, improving end-user accessibility and usability. Our experiments show that the model can identify in real time with excellent accuracy and efficiency. Our technology strengthens network resilience and protects dynamic online environments from disruptive cyber threats by empowering stakeholders with proactive DDoS mitigation capabilities.
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