DDoS-Detection and DDos-Detection-using-ML
About DDoS-Detection
AnshumanMohanty-2001/DDoS-Detection
Detailed Comparative analysis of DDoS detection using Machine Learning Models
This project helps network security professionals and system administrators identify Distributed Denial of Service (DDoS) attacks. It takes network traffic data (like packet information) and uses various machine learning techniques to determine if an attack is underway. The output is a classification indicating whether the network activity is normal or part of a DDoS attack.
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work