BOTNET Detection in IoT Environments

Botnet attacks have become a serious threat in modern IoT networks. As connected devices continue to grow, attackers find new ways to compromise them and use them for malicious activities. We channeled our efforts into building a dedicated digital sentinel capable of spotting dangerous botnet activity the moment it appears. The system's operational core is the Random Forest model, which functions as the primary classification engine. This model analyzes key network traffic features to instantly classify data as either benign operation or a hostile digital intrusion. The technology is supported by a robust architecture: Python's Flask manages the backend processing, efficiently handling data ingestion and high-speed predictions. Concurrently, the user-facing dashboard, implemented using React.js, provides a clean interface for data submission and visualization of real-time results. Critically, the system maintains a comprehensive security log, recording every detection incident to build a valuable historical threat profile for subsequent analysis. The result is a simple, high-performance, and highly accurate solution designed to significantly enhance security across the entire IoT environment.