ChronoNet Sentinel
A predictive network administration tool that uses machine learning on historical data to forecast failures. When an issue occurs, it employs a unique 'time-inversion' engine for instant root cause analysis.
C-Beam: The Voight-Kampff Protocol for Networks
An AI-powered network administration tool that detects rogue devices and malicious processes by analyzing their behavioral anomalies. It acts as a digital detective, flagging entities that deviate from established operational norms, much like a Blade Runner's Voight-Kampff test.
Story & Concept:
In the sprawling, chaotic 'dataspheres' of modern corporate networks, threats are no longer simple viruses; they are sophisticated 'Constructs'—compromised IoT devices, advanced persistent threats, or rogue AI agents designed to mimic legitimate traffic. Inspired by the grim realities of 'Blade Runner' and the vast, unknowable threats of 'Hyperion's' TechnoCore, this project posits that network administrators are modern-day Blade Runners. They need a tool not to match signatures, but to test for authenticity. 'C-Beam' is that tool. It administers a continuous, automated 'Voight-Kampff' test to every entity on the network, looking for the subtle deviations in behavior—the digital equivalent of a lack of empathy—that expose the machine hiding within the system. It automates the workflow of a digital detective, hunting for threats that hide in plain sight.
How It Works:
1. Baseline Phase (Memory Implantation): The system is deployed and enters a passive learning mode for 7-14 days. Using open-source sensors like Zeek, it monitors network traffic (netflow, protocol usage, connection patterns) and optional lightweight agents on key servers to build a detailed behavioral baseline. This baseline is the 'memory' of what is a normal, 'human' operational pattern for this specific network.
2. Interrogation Phase (Continuous Testing): After the baseline is established, C-Beam actively monitors all network entities (devices, services, user accounts). It uses a lightweight machine learning model (e.g., Isolation Forest or an Autoencoder) to constantly compare real-time behavior against the established baseline. The model's strength is its ability to detect anomalies without prior knowledge of specific malware, focusing only on what deviates from the learned 'normal'.
3. Anomaly Scoring (The Empathy Quotient): The system calculates an 'Empathy Score' for each entity, measuring its adherence to the network's norms. It analyzes vectors like:
- Protocol Empathy: A database server suddenly using FTP or IRC.
- Temporal Empathy: A 9-to-5 user account suddenly showing heavy activity at 3 AM.
- Traffic Empathy: A security camera that normally uses kilobytes of data suddenly uploading gigabytes to an unknown external address.
4. Reporting (Retirement Recommendation): When an entity's 'Empathy Score' drops below a critical threshold, it is flagged. C-Beam generates a concise, noir-style 'Case File' for the administrator. Instead of a generic alert, the report reads: "Case File: 9F5B. Subject: `iot-camera-lobby`. Deviation: Exhibits data exfiltration patterns inconsistent with its established identity profile. Recommend immediate network quarantine."
Implementation for Individuals:
This project is designed for solo developers using an accessible, low-cost stack. The core can be a Python server (Flask/Django) with a simple web dashboard (Chart.js). Data collection relies on a single instance of the open-source Zeek tool monitoring a network tap, with data processed by Scikit-learn models. This avoids expensive licensing and hardware, making it a perfect portfolio or startup project. Its earning potential lies in offering it as a niche, affordable SaaS solution for small to medium-sized businesses that lack the budget for enterprise-grade behavioral analytics platforms.
In the sprawling, chaotic 'dataspheres' of modern corporate networks, threats are no longer simple viruses; they are sophisticated 'Constructs'—compromised IoT devices, advanced persistent threats, or rogue AI agents designed to mimic legitimate traffic. Inspired by the grim realities of 'Blade Runner' and the vast, unknowable threats of 'Hyperion's' TechnoCore, this project posits that network administrators are modern-day Blade Runners. They need a tool not to match signatures, but to test for authenticity. 'C-Beam' is that tool. It administers a continuous, automated 'Voight-Kampff' test to every entity on the network, looking for the subtle deviations in behavior—the digital equivalent of a lack of empathy—that expose the machine hiding within the system. It automates the workflow of a digital detective, hunting for threats that hide in plain sight.
How It Works:
1. Baseline Phase (Memory Implantation): The system is deployed and enters a passive learning mode for 7-14 days. Using open-source sensors like Zeek, it monitors network traffic (netflow, protocol usage, connection patterns) and optional lightweight agents on key servers to build a detailed behavioral baseline. This baseline is the 'memory' of what is a normal, 'human' operational pattern for this specific network.
2. Interrogation Phase (Continuous Testing): After the baseline is established, C-Beam actively monitors all network entities (devices, services, user accounts). It uses a lightweight machine learning model (e.g., Isolation Forest or an Autoencoder) to constantly compare real-time behavior against the established baseline. The model's strength is its ability to detect anomalies without prior knowledge of specific malware, focusing only on what deviates from the learned 'normal'.
3. Anomaly Scoring (The Empathy Quotient): The system calculates an 'Empathy Score' for each entity, measuring its adherence to the network's norms. It analyzes vectors like:
- Protocol Empathy: A database server suddenly using FTP or IRC.
- Temporal Empathy: A 9-to-5 user account suddenly showing heavy activity at 3 AM.
- Traffic Empathy: A security camera that normally uses kilobytes of data suddenly uploading gigabytes to an unknown external address.
4. Reporting (Retirement Recommendation): When an entity's 'Empathy Score' drops below a critical threshold, it is flagged. C-Beam generates a concise, noir-style 'Case File' for the administrator. Instead of a generic alert, the report reads: "Case File: 9F5B. Subject: `iot-camera-lobby`. Deviation: Exhibits data exfiltration patterns inconsistent with its established identity profile. Recommend immediate network quarantine."
Implementation for Individuals:
This project is designed for solo developers using an accessible, low-cost stack. The core can be a Python server (Flask/Django) with a simple web dashboard (Chart.js). Data collection relies on a single instance of the open-source Zeek tool monitoring a network tap, with data processed by Scikit-learn models. This avoids expensive licensing and hardware, making it a perfect portfolio or startup project. Its earning potential lies in offering it as a niche, affordable SaaS solution for small to medium-sized businesses that lack the budget for enterprise-grade behavioral analytics platforms.