ETAS: An Efficient Trust Assessment Scheme for BANs

Abstract

A wireless body area network (WBAN) is a wireless network of wearable computing devices and intelligent physiological sensors. The intelligent physiological sensors collect and process sensitive data from the patient body. The security, reliability, and trustworthiness of sensitive data collected and processed by intelligent physiological sensors are critical due to its unique application domain. Moreover, consistent and reliable data gathering along with its transmission also plays a vital role in WBANs. Trust management (in BANs) has been found as a useful tool to improve cooperation among sensor nodes, security as well as reliability. The paper recommends a novel and efficient, lightweight trust assessment scheme (ETAS) suitable for health application domains and does not rely purely on any encryption technique. The primary purpose is to develop an exciting comprehensive, novel trust estimation framework for BANs to enhance reliability, dependability, security by isolating compromised (hotspot) nodes with great resource efficiency. ETAS incorporates several exclusive (unique) features like efficient trust evaluator, secure and attack resistant, along with competent trust aggregator function to achieve comprehensive trust score. The trust evaluator function is a multi-trust (communication trust, data trust, and energy trust) strategy to deal with severe internal security threats such as badmouthing attack, ballot-stuffing attack, sybil attack, traitor attack, etc. with less resource consumption. Moreover, ETAS incorporates both the success rate and misbehavior component during trust evaluation. The success rate record the number of successful/unsuccessful interactions among sensor nodes in terms of packet send/receive. The misbehavior component keeps records of current and past misbehavior of sensor nodes for effective decision making. Moreover, ETAS focus on the frequency of interaction among biomedical sensor nodes within a specified period to analyze their behavior for efficient trust decisions. Furthermore, ETAS incorporates temperature, data trust as well as the trust score of biomedical sensors to identify hotspot nodes in BSN. ETAS offers full flexibility to adjust the trust threshold, trust domain, reward, and punishment term according to system and application requirements. ETAS's efficiency is validated through several outcomes (MATLAB R2019a) along with theoretical analysis in terms of energy consumption, attack detection, mitigation, trust computation cost, and packet delivery ratio.

A Secure and Dependable Trust Assessment (SDTS) Scheme for Industrial Communication Networks: Analytical Approach

“…If nodes are frequently interacting, then cooperative interactionbased trust evaluation detects the malicious behavior of SNs and eliminates these SNs to reduce the FPR. Whenever the success rate is low, i.e., nodes are rarely interacting, then the non-cooperative interaction-based trust evaluation computes the rate of misbehavior, the weight of misbehavior, aggregate misbehavior, and frequency of misbehavior using our previous work [14] to eliminate such malicious nodes to reduce the FPR. The non-cooperative interaction-based trust evaluation mainly detects on-off nodes that change their behavior frequently.…”

“…If SNs interact in a sufficient number of times within the time window period, we compute communication (direct, indirect) trust and data trust. Furthermore, we check the deviation degree, data rate delivered, and data consistency using [13][14] to identify the malicious SNs. Figure 9 shows the average energy consumption in the presence of malicious nodes.…”

“…The existing trust management schemes [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] failed to fulfill the most fundamental requirement for industrial WSN (ICN). The survival of industrial WSN (ICN) depends on the accuracy of the sensed data, which is further dependent on the trust model.…”

“…T max is the maximum trust value used in the research work. During experimental analysis, we chose 10 as T max value.We have already defined communication trust, data trust, indirect trust, energy trust, throughput, misbehavior trust, misbehavior component, dynamic slide length, and abnormal attenuation factor (q) in our previous work [13][14]. x,y D + 1…”

“…It employs an innovative trust function, which itself punishes and rewards malicious and genuine nodes, respectively.  It employs direct communication, data, indirect, and current and past misbehavior, weight, and frequency of misbehavior[13][14]. It is unique in terms of its trust computation process, trust evaluation criteria, elimination of untrustworthy SNs, and assigning appropriate weightage since trust calculation based on success ratio reduces power consumption.Effectively defend against severe attacks such as an on-off, bad-mouthing, grey hole, garnished, selective forwarding, and whitewashing attacks.…”