Abstract
Securing cloud-assisted Wireless Body Area Network (WBAN) environment by applying security mechanism that consumes less resources is still a challenging task. This research makes an attempt to address the same. One of the most prominent attacks in cloud-assisted WBAN is Distributed Denial of Service (DDoS) attack that not only disrupts the communication but also diminishes the network bandwidth and capacity. This work is an extension of our previous research work in which an Enhanced Very Fast Decision Tree (EVFDT) was proposed which could detect DDoS attack successfully. However, in our previous work, the proposed algorithm is evaluated on the dataset generated by implementing LEACH protocol in NS-2. In this paper, a real-time cloud-assisted WBAN test bed is deployed to investigate the efficiency and accuracy of proposed EVFDT algorithm for real-time sensor network traffic. To evaluate the performance of proposed algorithm on real-time WBAN, four metrics are used including classification accuracy, time, memory, and computational cost. It was observed that EVFDT outperforms the existing algorithms by maintaining better results for these metrics even in the presence of extreme noise. Experimental results show that the EVFDT algorithm attains significantly high detection accuracy with less false alarm rate.