DDoS-Detection and DDos-Detection-using-ML
About DDoS-Detection
ReubenJoe/DDoS-Detection
Detailed Comparative analysis of DDoS detection using Machine Learning Models
This project helps network security teams and operations engineers identify Distributed Denial of Service (DDoS) attacks. It takes network traffic data as input and uses various machine learning models to classify whether the traffic is normal or part of a DDoS attack. The output helps security professionals quickly detect and respond to these critical threats.
About DDos-Detection-using-ML
saicharansigiri/DDos-Detection-using-ML
As we know that Now-a-days Most of the DDos attacks are often sourced from Cloud and Affect many systems and businesses, resulting in significant financial and intellectual property losses. It is critical to prevent this from occurring, so we used machine learning models to detect these attacks and block the source & further preventing them from occurring again .
This project helps network security teams proactively identify and block Denial of Service (DDoS) attacks originating from cloud environments. By analyzing network traffic data at the source, it detects malicious activity and pinpoints the attack origin. This allows network administrators and security analysts to prevent attacks before they impact systems.
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