kedarghule/DetectingDDosAttacks
The project implements and tests various machine learning algorithms to better classify and detect DDoS attacks.
This project helps network security analysts automatically identify Distributed Denial of Service (DDoS) attacks. It takes network traffic data, including IP addresses and timestamps, and classifies it as either a normal benign connection or a DDoS attack. This tool is designed for network defenders and security operations center (SOC) personnel.
No commits in the last 6 months.
Use this if you need to quickly and accurately detect DDoS attacks within your network traffic to protect service availability.
Not ideal if you need to identify more complex or zero-day attack types beyond standard DDoS patterns, or if you're not working with structured network flow data.
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Jupyter Notebook
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Last pushed
Jun 15, 2022
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