Information theoretic‐based detection and removal of slander and/or false‐praise attacks for robust trust management with Dempster‐Shafer combination of linguistic fuzzy terms

Abstract

Critical systems are progressively abandoning the traditional isolated and closed architectures, and adopting more federated solutions, in order to deal with orchestrated decision making within large-scale infrastructures. Such an increasing connectivity and the possibility of dynamically integrate constituents in a seamless manner by means of a decoupling middleware solution are causing the flouring of novel and previously unseen security threats, such as internal attacks conducted by camouflaged and/or compromised federated systems. Trust management is the most efficient way for dealing with such attacks, so that each constituent computes a trust degree of the other interacting ones based on the direct experiences and of collected reputation scores. An adversary may negatively affect the overall process with false reputations, which must not be considered when estimating a trust degree. Our work combines a multi-criteria linguistic fuzzy term formulation of the trust degree with the concept of entropy for measuring the divergence of certain scores from the other ones and to avoid to consider them during reputation aggregation. A set of experiments have been conducted in order to measure the quality and effectiveness of the presented approach. KEYWORDSDempster-Shafer theory, entropy, fuzzy sets, information theory, trust management INTRODUCTIONThe current most challenging ICT contexts is composed by the so-called System of Systems (SoS), 1 which consists in the realization of large-scale infrastructures as the federation of several systems, most of which already exist and need to be properly adapted so as to be interconnected. In such a vision, the federated systems are intended to team up in order to perform complex tasks from the composition of the operations and resources offered by the single systems. This is need in order to offer more functionalities, higher quality and efficiency than simply what is achievable by summing up what offered by the constituent systems. Despite the lacking of a standard definition of SoS and the availability in the current literature of around 40 different definitions, 2 several instances of SoS are available in the industrial practice, starting from the defense sector, where therehas been the first application of SoS, until other civil applications such as health information systems, critical infrastructure management, or the design of the Internet and cloud platforms (just to cite some of the main ones). All of those instances share a common architecture made of a federation middleware, typically complaint to the publish/subscribe service 3 for scalability and decoupling reasons, that seamlessly, autonomously, and dynamically let heterogeneous systems to be joined and integrated according to a plug-and-play approach without a centralized management.Many of the critical infrastructures under development embrace the vision of the SoS in order to have a better orchestration of the control decisions among their constituents, rather than having a set of independent systems with no...

MADPTM: Mix zones and dynamic pseudonym trust management system for location privacy

“…The same trick used to support bogus event message can still work. 9,10 (3) Various attacks, if malicious vehicles link ability to normal vehicle, cheat the message receiver by altering the trust management of the event message; a vehicle will be misled due to too many malicious vehicles forwarding the event message to maintain bogus messages and against the normal messages. [11][12][13] (4) Pseudonym expiry is also another problem of the current trust management systems.…”

“…(2) In a multi‐hop event scenario, if adversaries forward contradicting opinions earlier to trustworthy vehicles, then the honest vehicle may be misled. The same trick used to support bogus event message can still work . (3) Various attacks, if malicious vehicles link ability to normal vehicle, cheat the message receiver by altering the trust management of the event message; a vehicle will be misled due to too many malicious vehicles forwarding the event message to maintain bogus messages and against the normal messages .…”

A Survey on Trust Modeling from a Bayesian Perspective

“…[9] In addition to existing scattered definitions of trust, models for expressing trust involved in networked systems also lack coherence and consistency. Examples of the main formal techniques for modeling trust include fuzzy logic [10,11,12,13,14,15,16,17,18], subjective logic [19,20,21,22,23,24,25,26,27,28,29], Dempster-Shafer theory [30,31,32,33,34,35], ratings [36,37,38,39,40], weighting [41,42,43,44,45], neural network [46,47], Bayesian networks [48,49,50,51], game theory [52,53], swarm intelligence [54,55,56,57...…”

An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

“…However, intelligent inside attackers in WSNs may be able to discover security vulnerabilities in trust mechanisms by investigating their operations, and thus, they can avoid the detection of trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known as intelligent insider attacks [9,15,16]. In these attacks, attackers provide an evaluating sensor with false information to hamper accurate trust evaluation.…”