DDOS Attack Detection with Machine Learning: A Systematic Mapping of Literature

This is an era where at every stretch a person is becoming susceptible to a cyber attack To avoid these cyber attacks or prevent people from becoming a victim of malicious attacks, various technical measures have been taken. Various research has been conducted on the detection and prevention echniques. Machine Learning plays a major role in detecting cyber-attacks. This paper discusses about Distributed Denial of Service Attack (DDOS). These are one of the harmonious malwares that intend to increase the false network traffic resulting in engulfing the targeted website. As the technology advancing these types of attacks have been forming its types in the form of size, traffic and modes. On analyzing the research paper different algorithms like Random Forest, Convolution Neural Networks etc. have been implemented on different datasets. Some research papers divided the dataset into two parts and implemented the machine learning algorithms to get good precision and accuracy. Some researchers implemented two approaches: one the mathematical model and other is the machine learning model. Through these models a throughput analysis was done to have a better accuracy, resolution time and precision of the proposed model. This paper discusses about all such research work done in this field and provides a detailed analysis of the DDOS attacks detection algorithms.