Biblio

One of a high relentless attack is the crucial distributed DoS attacks. The types and tools for this attacks increases day-to-day as per the technology increases. So the methodology for detection of DDoS should be advanced. For this purpose we created an automated DDoS detector using ML which can run on any commodity hardware. The results are 98.5 % accurate. We use three classification algorithms KNN, RF and NB to classify DDoS packets from normal packets using two features, delta time and packet size. This detector mostly can detect all types of DDoS such as ICMP flood, TCP flood, UDP flood etc. In the older systems they detect only some types of DDoS attacks and some systems may require a large number of features to detect DDoS. Some systems may work only with certain protocols only. But our proposed model overcome these drawbacks by detecting the DDoS of any type without a need of specific protocol that uses less amount of features.