This desktop cybersecurity application analyzes running processes and predicts potential malware threats using a local machine learning model. It continuously monitors system metrics like CPU usage, memory consumption, network activity, and total running processes to detect suspicious behavior.
- Real-time Process Monitoring: Tracks active processes and system resource usage.
- AI-Powered Risk Assessment: A TensorFlow/Keras model assigns a risk score (0-100) based on process behavior.
- Malware Detection Logic: Flags processes exceeding predefined CPU/memory thresholds as potential threats.
- Threat Activity Level: Instead of random values, it calculates an actual threat level based on ML predictions.
- Frontend: PyQt5 (GUI)
- System Monitoring: psutil
- Machine Learning: TensorFlow Lite for local inference
- Backend Logic: Python
Lightweight & Local – No cloud dependency, runs entirely on your system.
Real-time Monitoring – Continuously updates process data and security insights.
AI-Powered Detection – Uses machine learning to assess process risks.
User-Friendly Interface – Simple and interactive PyQt5 GUI.