• Kamasani Bhanu Prakash
• Kamasani Tereena
• Mounika S Ganta
• D Arun Reddy
• Bhagya M

With great development in Science and Technology, the privacy and security of various organizations are condensed. Computer Intrusion and attack detection has always been a significant issue in networked environment. In most cases, there are two levels in which an intrusion may takes place i.e., in system level and the network level. Distributed Denial of Service is one of the network level attack. Distributed Denial of Service (DDoS) attack results in non-availability of services to the user. In case of organizations, this attack can result in a huge loss in terms of money or reputation since the clients of the organization cannot utilize the resources provided by that particular organization. The proposed solution to overcome this kind of attacks is, to monitor the network that is being attacked. The monitored network is analyzed and few parameters are considered from the analyzed network. These parameters are given as input data sets to machine learning algorithms for the classification of the data set. The algorithm classifies the data sets for the packets, causing the attack. These packets are then identified and terminated from the network that is being monitored.

DDoS,Machine Learning,Minimet, Scapy, SVM, SOM, Wireshark

"Detection of DDoS Attacks Using Hybrid Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 2, page no.596-597, February-2020, Available :http://www.jetir.org/papers/JETIR2002296.pdf

"Detection of DDoS Attacks Using Hybrid Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 2, page no. pp596-597, February-2020, Available at : http://www.jetir.org/papers/JETIR2002296.pdf