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View Code? Open in Web Editor NEWYet Another DDoS Detector
Yet Another DDoS Detector
๐ Task: Contribute to our project by adding your own dataset for DDoS detection. Whether it's a unique collection of packet data or network traffic captures, your dataset can enhance the diversity and robustness of our analysis.
Dataset Format:
Dataset Information:
Data Standardization:
README Update:
/data/CONTRIBUTING.md
for guidelines on dataset contribution and /data/README.md
for instructions on updating the README.๐ 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.
/data/CONTRIBUTING.md
for information on dataset usage and /scripts/model_evaluation.ipynb
for existing evaluation conventions.This analysis focuses on identifying DDoS attack signatures in a sample network dataset. We aim to spot anomalous patterns indicating potential DDoS attacks.
The dataset includes packet headers, payload content, and relevant features, covering both normal and potential attack instances.
reports/data
folder in pdf
formatData Exploration:
Feature Extraction:
Signature Definition:
Signature Spotting:
add dataset
Fork and Add Datasets: Fork the repository, clone it locally, create a new branch, add datasets in /data/
following guidelines, and submit a pull request.
Contribution Guidelines: Refer to /data/CONTRIBUTING.md
for detailed instructions on dataset format and ensure adherence to specified column structure.
๐ Task: Develop a Python notebook to facilitate the integration of three provided datasets. The goal is to standardize labels and features, ensuring consistency across all datasets. Additionally, implement data cleaning procedures to enhance data quality. Finally, store the processed data in variables X (features) and Y (labels), with labels being binary (0 or 1) to represent the presence or absence of a DDoS attack.
Data Reading:
Standardization:
Data Cleaning:
Label Standardization:
Variable Assignment:
Documentation:
Testing:
Guidelines:
Fork and Add Datasets: Fork the repository, clone it locally, create a new branch, add datasets in /data/
following guidelines, and submit a pull request.
Contribution Guidelines: Refer to /data/CONTRIBUTING.md
for detailed instructions on dataset format and ensure adherence to specified column structure.
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