Attacks Classification and a Novel IDS for Detecting Jamming Attack in WBAN

Abstract

Wireless Body Area Network (WBAN) aims to monitor patient's health remotely, by using mini medical sensors that are attached on the human body to collect important data via the wireless network. However, this type of communication is very vulnerable to various types of attacks, poses serious problems to the individual's life who wears the nodes. In this paper, we present a new classification of the most dangerous attacks based on different criteria, which gives us a clear vision of how attacks affect a WBAN system. Moreover, this classification will help us to specify the strength and the weakness of each attack in order to facilitate the development of a new intrusion detection system (IDS). In the second part of this work, we develop a novel IDS for detecting three types of jamming attacks in WBAN. The proposed methodology is based on the network parameters as an indicator to differentiate the normal case from the abnormal case like false alert or attack state. Through simulation analysis that was applied on Castalia platform by using OMNET++ as a simulator, proves that the proposed IDS have a great effect for detecting the presence of jamming attack in the network.

A Survey on Wireless Wearable Body Area Networks: A Perspective of Technology and Economy

“…If such information is stolen, it can be used for illegal activities that could result in losses to the owner in the shape of mental disturbance or financial blackmailing. For brevity, a generic set of WBANS attacks, security requirements and measures have been given in Table 7 [ 131 , 132 , 133 , 134 ].…”

“…Another perspective of the attacks and issues is to view them from a WBAN network stack point of view. We have observed from [ 131 , 133 ] that jamming and tampering are major issues at the physical layer. On the link layer, collision, unfairness, denial of sleep, traffic analysis, spoofing, eavesdropping and exhaustion are some of the issues that affect the security of WBAN devices.…”

Challenges and Developments in Secure Routing Protocols for Healthcare in WBAN: A Comparative Analysis

“…Suppose if an attacker disables the ECG (electrocardiogram) sensor of a heart disease patient; this would lead the patient into critical situation or even death [38]. The WBAN literature has suggested various ways to cope up with DoS attack [43][44][45][46][47][48]. Localization service finds the location of biosensors in a dynamic network.…”

Intrusion detection based on fuzzy logic for wireless body area networks: review and proposition

“…After that, the coordinator node communicates with one or more point access using for instance WiFi; this part is named Inter-WBAN communication. Then, the third level is called beyond-WBAN communication in which the medical data is received to the medical center via mobile network [1], [2]. In the event that the sensor collects abnormal data, it will launch an alert to the medical team.…”

“…Figure 1. The scenario of WBAN with an attack [2] Over the years, the artificial intelligence provides many benefits for implementing IDS, by taking care of the energy consumption rate [6]. There are several types of attack detection that apply the mechanisms of AI technologies, such as support vector machine (SVM), game theory, neural networks, decision tree and fuzzy inference systems (FIS), which allowed to improve the quality of IDS.…”