HALcyon: Predictive Anomaly Detection System

HALcyon's story begins with the observation that most security systems are reactive, only responding after a breach. Inspired by the predictive abilities hinted at in Dan Simmons' Hyperion (specifically the TechnoCore's ability to anticipate events) and the ever-watchful, problem-solving nature of HAL 9000 from 2001: A Space Odyssey, HALcyon aims to be proactive. The concept is to create a security system that learns the 'normal' patterns of a building's environment from sensor data (motion sensors, temperature sensors, door/window sensors, etc.). This data is fed into a machine learning model trained to predict future sensor readings. When the actual sensor readings deviate significantly from the predicted readings – indicating an anomaly – HALcyon triggers an alert. Implementation is relatively straightforward. A Raspberry Pi or similar single-board computer acts as the central hub. Open-source machine learning libraries (e.g., TensorFlow Lite, scikit-learn) are used to train and deploy the anomaly detection model. Sensor data can be collected using readily available and inexpensive IoT sensors. The 'AI Workflow for Companies' scraper project inspires the approach of automating the data collection and model retraining processes. The system continuously learns and adapts to the environment, improving its accuracy over time. This niche approach focuses on preemptive security, differentiating it from typical alarm systems. The low cost is achieved through the use of open-source software and affordable hardware. High earning potential lies in offering HALcyon as a subscription-based service to small businesses or homeowners who want a more sophisticated security system without the high price tag of enterprise-level solutions. Additionally, specialized models can be developed for specific environments, such as data centers or warehouses, increasing the value proposition.