DDoS Detection Using Machine Learning Ensemble


In the era of internet and online connectedness, where data is the most valuable asset, it is ever important for an organization to protect itself and it’s assets from various security threats. One of these threats is a Distributed Denial of Service (DDoS) attack that can cut off the network service by overwhelming the targeted server or network by flooding it with superfluous requests in an attempt to overload the server to prevent legitimate requests from being fulfilled. DDoS attacks utilize multiple compromised systems as sources of internet traffic to increase their effectiveness. What makes DDoS attacks more lethal is that fighting them requires differentiating legitimate requests from illegitimate ones. A site or service unexpectedly being sluggish or inaccessible is the most obvious symptom of a DDoS attack. But since a number of causes like legitimate spike in network traffic can create similar issues, further investigation is necessary. Keywords : DDoS Detection; Intrusion Detection; Machine Learning; Distributed Denial of Service



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL