Abstract
Cloud is known as a highly-available platform that has become most popular among businesses for all information technology needs. Being a widely used platform, it’s also a hot target for cyber-attacks. Distributed Denial of Services (DDoS) is a great threat to a cloud in which cloud bandwidth, resources, and applications are attacked to cause service unavailability. In a DDoS attack, multiple botnets attack victim using spoofed IPs with a huge number of requests to a server. Since its discovery in 1980, numerous methods have been proposed for detection and prevention of network anomalies. This study provides a background of DDoS attack detection methods in past decade and a survey of some of the latest proposed strategies to detect DDoS attacks in the cloud, the methods are further compared for their detection accuracy.
Highlights
Introduction & BackgroundCloud Computing has become the most convenient and innovative architecture for businesses globally for information technology (IT) needs
Another entropy based model presented by Sunny Behal [12] use information theory metrics φ-Entropy & φDivergence to detect Flast Events (FE) and Distributed DoS (DDoS) attacks in cloud, it assumes that all attack sources works in coordination and have similar logic toward victim
Comparison:This section provides summary about the results achieved by different ddos attack detection methods discussed in last section for their detection rate accuracy
Summary
Introduction & BackgroundCloud Computing has become the most convenient and innovative architecture for businesses globally for information technology (IT) needs. Chonka et al [34] to differentiate legit and malicious traffic and introduced neural network based model to detect DDoS attacks [12].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
