Cloud Based DDOS Attack Detection Using Machine Learning Architectures: Understanding the Potential for Scientific Applications - Manjunath C R; Ketan Rathor; Nandini Kulkarni; Prashant Pandurang Patil; Manoj S. Patil; Jasdeep Singh (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Cloud computing technology has become a crucial component of IT services utilized in daily living in this era of technology. Website hosting services are gradually migrating to cloud in this regard. This increases the value of cloud-based websites while also creating new risks for those services. A severe threat of this nature is DDoS attack. This research propose novel technique in cloud based DDOS attacks using machine learning architectures. here the input has been collected based on cloud module and it has been processed for dimensionality reduction and noise removal. Then this data feature has been extracted and classified using ResNet-101 based KELM. The experimental analysis has been carried out in terms of data delivery ratio, transmission rate, validation accuracy, training accuracy, end-end delay. the proposed technique attained data delivery ratio of 92%, transaction rate of 82%, validation accuracy of 89%, training accuracy of 96%, end-end delay of 56%