Distributed denial of service (DDoS) attack detection using data mining approach in cloud-assisted wireless body area networks. (2016)

• Record Type: Journal Article
• Authors: Latif, Rabia; Abbas, Haider; Latif, Seemab
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 23:Number 1/2(2016)
• Page Start: 24
• Page End: 35
• Publication Date: 2016
• Subjects: cloud computing -- wireless BANs -- body area networks -- data mining -- DDoS -- distributed DoS -- denial of service attacks -- sensor localisations -- patient data -- healthcare services -- data security -- data privacy -- data availability; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

Nowadays, wireless body area networks (WBANs) is emerging as a promising technology with a considerable potential in improving patients healthcare services. The integration of WBAN and cloud computing technology provides a platform to create a new digital paradigm with leading features called cloud-assisted WBAN. The foremost concern of cloud-assisted WBAN is the security and privacy of data either collected and stored by WBAN sensors or transmitted to cloud over an insecure network. Among these, data availability is the most nagging security issue. The major threat to data availability is distributed denial of service attack (DDoS) normally launched from various distributed locations. In order to assure the all time availability of patients data, we propose a distributed victim based DDoS attack detection mechanism based on very fast decision tree (VFDT) learning model in cloud-assisted WBAN. The evaluation and performance analysis shows that the proposed mechanism could detect DDoS attack with high accuracy, and reduced false positive and false negative ratio.