Developing the Model - Githubissues

DRedDevil04 commented 10 months ago

DDoS Detection Model Challenge

Issue Description

🚀 Challenge: Compete to create the most accurate model for detecting DDoS attacks! Participants are tasked with developing a machine learning model that assigns labels of 1 for DDoS attacks and 0 for normal traffic based on packet features. Additionally, participants are required to maintain a list of source IPs associated with detected DDoS packets.

Evaluation Criteria

Model Accuracy: The accuracy of the machine learning model in correctly classifying packets.
False Positive Rate: Minimize false positives to enhance precision.
List of Source IPs: Maintain an accurate list of source IPs for all packets classified as DDoS attacks.

Data Details

Three datasets are provided for training and evaluation.
If you wish to add another dataset, feel free to contribute to the other open-for-all issue and get your data added.
Features include packet metadata such as IP addresses, TCP/UDP ports, and flags.
Labels should be binary, with 1 denoting DDoS attacks and 0 denoting normal traffic.

Submission Guidelines

Participants are encouraged to use a variety of machine learning algorithms and techniques.
Submissions should include a Jupyter notebook or Python script containing the model implementation.
Clearly document and comment your code for transparency and understanding.

Reward

The participant with the most accurate model and well-maintained list of source IPs will be recognized and only their PR will be merged.

Additional Information

Participants can discuss their approaches, findings, and seek clarifications in the project's discussions.
Please adhere to project coding standards and guidelines during implementation.

Note

Refer to /data/CONTRIBUTING.md for information on dataset usage and /scripts/model_evaluation.ipynb for existing evaluation conventions.

sarthakvermaa commented 10 months ago

claim