Dev Flux is a centralized, software-defined platform designed to simulate, monitor, and record realistic edge device behavior under both normal and anomalous conditions.
Instead of relying on expensive physical hardware such as routers, IoT devices, or authentication servers, Dev Flux creates virtual sensors that behave like real systems using:
- Mathematical models
- Time-series equations
- Controlled randomness
- Scenario-driven state transitions
The platform enables reproducible experimentation, machine-learning-ready dataset generation, and multi-device anomaly research, all through an intuitive web-based interface.
Modern cybersecurity and edge computing research depends heavily on almost realistic testbeds. However, most researchers and students face serious limitations:
- Physical devices are expensive
- Testbed setup is complex and time-consuming
- Existing simulators generate unrealistic random data
- Multi-device and coordinated attack scenarios are hard to model
- Reproducible datasets are difficult to produce
Dev Flux solves this problem by providing a fully software-based alternative that produces almost realistic, time-correlated, and labeled data, suitable for research, teaching, and experimentation.
Dev Flux allows users to:
- Simulate realistic device-level behavior
- Inject controlled anomalies
- Monitor behavior live through a centralized dashboard
- Replay historical runs for inspection and analysis
- Automatically generate ML-ready datasets with proper labeling
- Study multi-device and distributed attack patterns
- Experiment without any physical hardware
-
Hardware-Free Testbed
No routers, IoT boards, or servers required. -
Equation-Driven Sensor Models
Data evolves over time instead of appearing as random noise. -
Scenario-Based Experimentation
Attacks can be injected gradually, suddenly, or in coordinated waves. -
Reproducibility
Same scenario → same behavior → consistent datasets. -
ML Readiness
Clean labels, timestamps, and structured outputs for anomaly detection research. -
Educational Value
Ideal for demonstrations, labs, and learning environments.
Dev Flux is a single evolving project, developed iteratively to improve realism, usability, and research capability. Each version represents a clear milestone, not a separate idea. The repository reflects this journey transparently and intentionally. Below is a structured overview of each version.
Some sites may go to sleep when not in use. Just access the site and give it a minute to wake up. It will be online again shortly.
Dev Flux V0 establishes the functional foundation of the project. All core ideas, sensor models, dataset generation logic, and system behavior are present in this version.
Access Site: Dev Flux (Version 0).
- Virtual sensors for:
- Network (normal + anomalous)
- Authentication (normal + anomalous)
- Equation-driven, time-correlated data generation
- Threaded sensor execution
- Live monitoring through a web interface
- Historical run replay
- Dataset generation and download
- Clean labeling for ML use
- Launch the application web.
- Open the Sensors tab.
- Toggle individual sensors:
- Network
- Network Anomaly
- Authentication
- Authentication Anomaly
- Observe real-time data in live tables.
- Ensure all sensors are OFF.
- Go to Dataset Generator, enter name and turn dataset mode ON.
- The system automatically:
- Starts all sensors
- Synchronizes logs
- Applies ground-truth labels
- Stop dataset mode to:
- Package logs
- Generate metadata
- Create a ZIP dataset. [Download datasets directly from the UI]
Best for: ML pipelines, academic experiments, reproducible research.
- Flask backend
- JSONL-based logging
- Mathematical and probabilistic sensor models
- Bootstrap-based frontend
- Premium UI with animated interactions
- Prove feasibility of software-defined edge sensors
- Demonstrate realistic data generation
- Establish the complete end-to-end pipeline
Dev Flux V1 is functionally equivalent to V0. The system behavior, data models, and outputs remain the same.
Access Site: Dev Flux (Version 1).
- Minor UI interaction refinements:
- Smoother animations
- Enhanced visual feedback
- Internal code cleanup and structure improvements
- Improved readability and maintainability
- More consistent naming and organization
- Launch the application web.
- Open the Sensors tab.
- Toggle individual sensors:
- Network
- Network Anomaly
- Authentication
- Authentication Anomaly
- Observe real-time data in live tables.
- Ensure all sensors are OFF.
- Go to Dataset Generator, enter name and turn dataset mode ON.
- The system automatically:
- Starts all sensors
- Synchronizes logs
- Applies ground-truth labels
- Stop dataset mode to:
- Package logs
- Generate metadata
- Create a ZIP dataset. [Download datasets directly from the UI]
Best for: ML pipelines, academic experiments, reproducible research.
- Same sensor logic
- Same equation-based models
- Same dataset generation pipeline
- Same capabilities and outputs
- Polish the system without altering behavior
- Improve maintainability and clarity
- Prepare the project for further architectural expansion
Dev Flux V2 marks the first real architectural shift. The project transitions from manual sensor control to a scenario-driven testbed model.
Access Site: Dev Flux (Version 2).
- Scenario engine with time-based phases
- Automated switching between normal and attack states
- Coordinated behavior across multiple sensors
- Central testbed controller
- Testbed execution UI
- Select & Upload a scenario JSON file.
- Start the testbed.
- The system automatically:
- Switches between normal and attack phases
- Coordinates multiple sensors
- Monitor:
- Live sensor data
- Detected anomalies
- Stop the testbed to save the session.
- Load past sessions for analysis.
Best for: IDS testing, attack pattern evaluation, security research.
- Testbed fully implemented and operational
- Other system sections shown as informational placeholders
- Scenario JSON definitions
- Timeline-driven state management
- Centralized logging
- Testbed-oriented UI layout
- Introduce automation and repeatability
- Enable structured experiments
- Shift from manual control to orchestration
Dev Flux V3 refines V2 by narrowing focus entirely to the testbed. All non-essential UI sections are removed to create a clean, research-oriented interface.
Access Site: Dev Flux (Version 3).
- Testbed becomes the sole active UI module
- Simplified interface for live monitoring and replay
- Clear separation between:
- Monitoring
- Anomaly detection
- Historical analysis
- Select & Upload a scenario JSON file.
- Start the testbed.
- The system automatically:
- Switches between normal and attack phases
- Coordinates multiple sensors
- Monitor:
- Live sensor data
- Detected anomalies
- Stop the testbed to save the session.
Best for: IDS testing, attack pattern evaluation, security research.
- Same scenario engine
- Same testbed controller
- Same sensor logic and data generation
- Reduce UI noise
- Emphasize experimentation and observation
- Support academic and research demonstrations
Dev Flux VS is not a separate implementation, but a demonstration layer built on the same conceptual model.
Access Site: Dev Flux (Streamlit Version).
- Embedded, predefined scenarios
- Timeline-driven attack visualization
- Simplified controls for non-technical audiences
- No setup or backend requiredI
- Open the Streamlit app.
- Select scenario duration (1, 2, or 3 minutes).
- Click Run.
- Watch:
- Timeline progression
- Active attack indicators
- Attack summary table
- Pause, resume, or reset the simulation.
Best for: Quick demos, non-technical audiences.
- Streamlit-based interface
- Auto-refreshing simulation timeline
- Visual attack indicators
- Stateless demo execution
- Public demonstration
- Quick understanding of the system concept
- Basic Presentation and showcase use