Nexus Watcher: SCADA Anomaly Detector

A low-cost SCADA anomaly detection system that emulates a futuristic surveillance network, identifying deviations from normal operational parameters using publicly available SCADA data simulations.

Inspired by the granular data analysis of e-commerce pricing, the ominous undertones of 'Nightfall,' and the gritty, predictive surveillance of 'Blade Runner,' Nexus Watcher aims to democratize SCADA anomaly detection. The core concept is to simulate a 'Blade Runner'-esque Nexus-Unit monitoring industrial processes, specifically SCADA systems. Instead of illegal replicants, our 'anomalies' are deviations from expected operational norms within simulated SCADA data streams.

Story and Concept: In a near future, decentralized industrial oversight is crucial. Nexus Watcher acts as an affordable, individual-run surveillance node for SCADA systems. Users can deploy this tool to monitor publicly accessible (or simulated) SCADA data feeds from various utilities (water treatment, power grids, etc.). The system learns the 'normal' behavior of these systems, much like Deckard observes Nexus-6 models, and flags any statistically significant deviations. The 'Nightfall' influence comes in the subtle, persistent nature of the monitoring and the potential for uncovering hidden patterns or nascent failures.

How it Works:
1. Data Acquisition (Simulated/Public): The project will leverage publicly available SCADA data simulators (e.g., open-source SCADA simulation tools) or scrape readily available, anonymized data from open-source industrial control system dashboards (with strict ethical and legal considerations). The initial phase would focus on robust simulation.
2. Profile Generation: For each monitored parameter (e.g., water pressure, voltage, temperature), the system builds a statistical profile of its normal operational range and patterns over time. This is akin to understanding a subject's baseline behavior.
3. Anomaly Detection: The system continuously compares incoming data points against the established profile. Deviations exceeding a predefined threshold (e.g., several standard deviations from the mean, sudden changes in variance) trigger an alert. This is the 'detection' phase, similar to how a Nexus unit identifies anomalies in human behavior.
4. Alerting and Reporting: Alerts are generated via a simple dashboard (web-based, easily hosted locally) or through notifications (email, SMS). Reports can detail the detected anomaly, its timestamp, and the affected parameters.

Niche and Low-Cost Implementation: This project is niche as SCADA security is often enterprise-level. It's low-cost because it can be built with open-source libraries (Python: Pandas, NumPy, Scikit-learn for statistical analysis) and hosted on low-power single-board computers (Raspberry Pi) or even cloud-based free tiers. The focus is on accessible, intelligent monitoring, not complex hardware intrusion.

High Earning Potential: While the initial implementation is individual-focused, the technology can be scaled and monetized in several ways:
- Subscription Service: Offer premium dashboards, historical data analysis, or more advanced anomaly detection algorithms as a subscription for small industrial operators or IoT enthusiasts.
- Consulting: Provide consultation services for implementing and customizing Nexus Watcher for specific industrial needs.
- Specialized Modules: Develop and sell specialized anomaly detection modules for specific industrial sectors (e.g., a module for oil and gas pipelines, a module for water distribution).
- Data Insights Platform: Aggregate anonymized anomaly data to provide industry-wide trend analysis and predictive maintenance insights for a fee.

Nexus Watcher offers a unique blend of predictive surveillance and practical industrial monitoring, making advanced SCADA security accessible to individuals and small entities.

Project Details

Area: SCADA Systems Method: E-Commerce Pricing Inspiration (Book): Nightfall - Isaac Asimov & Robert Silverberg Inspiration (Film): Blade Runner (1982) - Ridley Scott