Security and privacy are the first and foremost concerns that should be given special attention when dealing with Wireless Body Area Networks (WBANs). As WBAN sensors operate in an unattended environment and carry critical patient health information, Distributed Denial of Service (DDoS) attack is one of the major attacks in WBAN environment that not only exhausts the available resources but also influence the reliability of information being transmitted. This research work is an extension of our previous work in which a machine learning based attack detection algorithm is proposed to detect DDoS attack in WBAN environment. However, in order to avoid complexity, no consideration was given to the traceback mechanism. During traceback, the challenge lies in reconstructing the attack path leading to identify the attack source. Among existing traceback techniques, Probabilistic Packet Marking (PPM) approach is the most commonly used technique in conventional IP- based networks. However, since marking probability assignment has significant effect on both the convergence time and performance of a scheme, it is not directly applicable in WBAN environment due to high convergence time and overhead on intermediate nodes. Therefore, in this paper we have proposed a new scheme called Efficient Traceback Technique (ETT) based on Dynamic Probability Packet Marking (DPPM) approach and uses MAC header in place of IP header. Instead of using fixed marking probability, the proposed scheme uses variable marking probability based on the number of hops travelled by a packet to reach the target node. Finally, path reconstruction algorithms are proposed to traceback an attacker. Evaluation and simulation results indicate that the proposed solution outperforms fixed PPM in terms of convergence time and computational overhead on nodes.
Keywords: Cloud- assisted WBAN; Distributed Denial of Service (DDoS) Attack; Probabilistic Packet Marking (PPM); Traceback.