SCADA Time Anomaly Forecaster (STAF)

Inspired by Asimov's Foundation (predicting future events through historical data analysis), Gilliam's 12 Monkeys (understanding cyclical events), and security log scrapers, STAF analyzes SCADA system security logs to identify and forecast future security incidents. Imagine a psychohistory applied to industrial control systems. The project involves building a system that ingests SCADA security logs (freely available datasets can be used for initial development). These logs are then processed to extract patterns and anomalies related to user activity, network traffic, and system events. A time series forecasting model (e.g., ARIMA, Prophet, or LSTM) is trained on this historical data to predict future anomalies. The system flags deviations from the predicted behavior as potential security breaches. The niche is focusing specifically on predicting the -timing- of attacks, rather than just detecting them after they happen. Low-cost implementation involves using open-source tools for log scraping (e.g., Python scripts using libraries like `requests` and `BeautifulSoup` to gather logs from accessible sources or simulating logs), data processing (Pandas, NumPy), and machine learning (Scikit-learn, TensorFlow/Keras). Earning potential stems from selling the anomaly forecasting model to SCADA system operators as a predictive security tool, offering vulnerability assessment services based on the model's output, or providing custom anomaly detection rules for specific industrial control systems. Selling customized rules and alert configurations to factories on a subscription basis or providing early alerts as a paid service create revenue streams.