Robocop

Robocop is a prototype intrusion detection system (IDS) with automated incident triage capabilities.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

Robocop's core detection system is an Ensemble method -- a collection of machine learning methods that aggregate their predictions and coordinate to analyze network traffic and keep the bad guys out.

At the base there is a lightweight monitoring the network for anomalous traffic. If anything out of the ordinary is found, the suspicious activity is then passed to a more granular classifier model to get a second set of opinions on what exactly is going on. Then the results of this model, along with metadata about the traffic are passed to an Agentic Large Language Model that creates a report for incident handlers while Robocop takes steps to counteract the detected attack (e.g. closing ports, killing processes, backing up data, etc.)

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Built With

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Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

Installation

1. Clone the repo

   git clone https://github.com/Neumann-Labs/Robocop.git

Usage

Coming Soon

For more examples, please refer to the Documentation

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Roadmap

1. Data Ingestion and Preprocessing:

• Implement robust data ingestion pipelines to collect network and host traffic data from various sources (e.g., network sensors, log files, endpoints).
• Preprocess the data to handle missing values, normalize features, and extract relevant information for analysis.
• Bootstrap with these data sources

2. Feature Engineering:

• Identify and extract meaningful features from the collected data that can effectively distinguish between normal and malicious activities.
• Consider using techniques like feature selection and dimensionality reduction (e.g. PCA) to optimize the feature set.

3. Ensemble of Machine Learning Models:

• Utilize a diverse set of machine learning algorithms, such as decision trees, random forests, support vector machines, and deep learning models, to build the ensemble.
• Train each model on different subsets or representations of the data to capture different patterns and anomalies.
• Implement a weighted voting mechanism to aggregate the predictions of individual models and make the final decision.
• Experiment with more advanced ensemble techniques such as Stacking, Bagging, Boosting, etc.

4. Agentic LLM Integration:

• Leverage the capabilities of an agentic LLM, such as AutoGPT or BabyAGI, to automate incident triage and response.
• Train the LLM on a large corpus of cybersecurity knowledge, including attack patterns, mitigation strategies, and incident response procedures.
• Integrate the LLM with the ensemble models to interpret the detected anomalies, generate incident reports, and recommend appropriate actions.

5. Real-time Monitoring and Alerting:

• Design a real-time monitoring component that continuously analyzes the incoming network and host traffic data.
• Implement an alerting mechanism to notify security analysts or trigger automated response actions when suspicious activities are detected.

6. Feedback Loop and Continuous Learning:

• Incorporate a feedback loop that allows security analysts to provide input on the accuracy and relevance of the generated incident reports and response actions.
• Use this feedback to fine-tune the ensemble models and the agentic LLM, enabling continuous learning and improvement over time.

7. Scalability and Performance:

• Ensure that the IDS system is designed to handle large volumes of data and can scale horizontally to accommodate growing network traffic.
• Optimize the system's performance by leveraging distributed computing frameworks, such as Apache Spark or Hadoop, for parallel processing of data.

8. Security and Privacy:

• Implement strong security measures to protect the IDS system itself from potential attacks and unauthorized access.
• Adhere to data privacy regulations and ensure that sensitive information is properly anonymized or encrypted.

9. Testing and Evaluation:

• Conduct thorough testing and evaluation of the IDS system using diverse datasets, including both known and unknown attack scenarios.
• Measure the system's performance metrics, such as detection accuracy, false positive rate, and response time, to assess its effectiveness.

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Nicholi Caron - @nicholicaron - [email protected]

Website: https://www.neumann-labs.com/

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Acknowledgments

TBA

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