ChronoCorp Anomaly Detector

Inspired by the 'Digital Reports' scraper project, 'Frankenstein', and '12 Monkeys', ChronoCorp Anomaly Detector is a niche automation tool designed to improve the resilience of existing industrial automation systems. The project's narrative stems from the idea that industrial automation systems, like Frankenstein's monster, are often cobbled together from disparate parts, creating vulnerabilities. Moreover, like in '12 Monkeys', small initial failures can cascade into large-scale system breakdowns.

The system works by:

1. Data Scraping & Ingestion: It uses a web scraper (similar to the 'Digital Reports' scraper project) to collect publicly available historical data on industrial control systems (ICS) failures, including details about the hardware, software, and environmental factors involved.
2. Frankenstein Identification: The system analyzes the ICS configuration files (obtained either directly from the client or by simulating a vulnerable system based on scraped data) to identify 'Frankenstein' elements: combinations of outdated software versions, incompatible hardware, and unsecured network configurations. It uses a scoring system to quantify the risk associated with each configuration.
3. Anomaly Prediction (12 Monkeys Cascading Failure Simulation): Based on the identified 'Frankenstein' elements and the historical failure data, the system simulates potential cascading failures. It uses Monte Carlo simulations and Bayesian networks to predict the likelihood and potential impact of various failure scenarios.
4. Automated Mitigation Recommendations: The system generates a prioritized list of mitigation recommendations, ranging from software updates and hardware replacements to network security enhancements and redundancy implementations. These recommendations are tailored to the specific vulnerabilities identified in the system.
5. Alerting System: Integrate with standard alerting platforms like Prometheus or Grafana to immediately notify operators of critical anomalies and recommended mitigations.

Implementation:

- Low Cost: Can be implemented using open-source tools (Python for scraping, data analysis, and simulation; open-source anomaly detection libraries; free database solutions).
- Niche: Focuses on identifying and mitigating vulnerabilities stemming from system composition and cascading failures, a relatively under-addressed area in standard anomaly detection.
- Individual Implementation: The core logic can be developed by a single skilled programmer. Scaling to handle larger datasets and more complex simulations can be addressed later.
- High Earning Potential: Companies are willing to pay significant amounts to prevent system downtime and improve the resilience of their automation systems. Potential revenue streams include:
- Subscription-based access to the analysis platform.
- Consulting services to implement the recommended mitigations.
- Training courses on how to use the system and improve system resilience.