Ensemble Based Feature Selection Method for DDoS (EBFM-DDoS) Attack Detection of Healthcare Data in the Cloud Environment

Cloud computing is a novel technological advancement that provides diverse range services to its users. The significant expansion of cloud computing has stimulated researchers to develop new business models by facilitating the exchange of critical computing resources online. Malicious entities used this technology to disrupt cloud services by launching Distributed Denial of Service (DDoS) assaults. These attacks might be a considerable threat in cloud infrastructure as they flood servers with traffic, leading to their incapacitation in healthcare data. The direct consequences of service failure include financial losses and heightened stress levels for professionals, which can be mitigated by detecting DDoS attacks early and preventing them from affecting the system. Unfortunately, detecting DDoS attacks is an exceedingly challenging task. Accordingly, a DDoS defense framework is essential to prevent and detect the attack or to mitigate its effect on healthcare data at the cloud environment using sophisticated learning techniques. For any learning model, collecting the data is a primary stage in which, especially for investigating the packets to identify the normal and the attack, a huge number of packet characteristics, termed as attributes, have to be collected, forming an input dataset. However, not all the characteristics of network data collected will be essential for the underlying study. Also, these irrelevant attributes always stimulate the performances of entire model and lead to misclassification. This chapter introduces the Ensemble-based Feature Selection Framework (EBFM-DDoS) which has the goal of determining relevant features for DDoS detection. This methodology amalgamates a range of feature selection approaches, namely wrapper, filter and embedded methods.