Networked Sentinels: Frankenstein's Ghost in the Machine

Inspired by Mary Shelley's 'Frankenstein' and the AI sentience explored in 'Ex Machina,' this project focuses on creating 'Networked Sentinels' – an easy-to-implement, niche network administration tool. The core idea is to build a system that 'learns' the normal behavior of a network, much like a scientist observes and learns about their creation. Using web scraping techniques from projects like 'Usage Statistics' scrapers, our tool will gather data on network traffic patterns, device activity, and system logs. However, instead of simply reporting statistics, this data will feed into a lightweight, on-device AI model. This model will be trained to identify anomalies that deviate significantly from the established 'norm.' These anomalies could represent early signs of a security breach (a 'glitch' in the network's 'body') or performance issues that could lead to a complete system 'failure.' The 'Frankenstein' element comes into play with the ethical considerations of AI monitoring – how do we ensure transparency and avoid 'over-surveillance' without stifling necessary observation? The 'Ex Machina' inspiration drives the focus on subtle AI behavior recognition. The tool will be low-cost, potentially using Raspberry Pis or similar low-power devices for deployment, and can be offered as a subscription service for small to medium-sized businesses or even individual power users who want enhanced network visibility. The niche aspect lies in its focus on -predictive- anomaly detection for small-scale networks, a segment often underserved by enterprise-level solutions. The high earning potential stems from the increasing reliance on robust and secure networks, and the demand for affordable, intelligent monitoring solutions.