Distributed Trust Management Scheme Research Articles

A bayesian-based distributed trust management scheme for connected vehicles’ security

A bayesian-based distributed trust management scheme for connected vehicles’ security

Joint Trust Management and Sharing Provisioning in IoV-Based Urban Road Network

Internet of Vehicles (IoV) is a novel technology to enhance the safety, intelligence, and efficiency of traffic systems, where vehicles can exchange critical information with other vehicles, roadside units, pedestrians, and cloud platforms. However, the dynamic network topology, high speed, and exposed communication links inevitably pose security threats to IoV. It is pivotal to establish a trust management and trust-sharing mechanism between vehicles to guarantee the safety of IoV. This paper proposes a distributed trust management scheme to discriminate malicious vehicles utilizing the machine learning technology Random Forest (RF). With the help of the sliding time window technology, the trust degree of vehicles can be comprehensively evaluated through the CART trees according to the current and historical records. To further improve the security of communication processes, we also introduce a lightweight cryptography mechanism. In addition, a trust-sharing mechanism based on path prediction algorithm is proposed to guarantee the consistency of trust information in the network. Finally, extensive simulations are conducted to demonstrate the feasibility and efficiency of the proposed scheme.

Dynamic distributed trust management scheme for the Internet of Things

The Internet of Things (IoT) comprises of a diverse network of homogeneous and heterogeneous nodesthat can be accessed through network ubiquitously. In unattended environments, the IoT devices are prone to variousattacks including ballot-stu?ing, bad-mouthing, self-promotion, on-off, opportunistic behavior attacks, etc. The on-offattack is di?icult to detect as nodes switch their behavior from normal to malicious alternatively. A trust managementmodel is a tool to defend the IoT system against malicious activities and provide reliable data exchange. The majorityof existing IoT trust management techniques are based on static reward and punishment values in pursuit of trustcomputation thereby allowing the misbehaving nodes to deliberately perform on-off attacks. Due to the static nature ofawarding scores, these schemes fail to identify malicious nodes in certain cases. In this paper, a dynamic and distributedtrust management scheme (DDTMS) is proposed where nodes can autonomously evaluate peer nodes' behavior anddynamically grant reward and penalty score. The proposed scheme successfully detects the on-off attack and isolates themisbehaving nodes thereby classifying them into three distinct categories based on their severity levels i.e. low, mild,and severe. Simulation-based performance evaluation shows improved performance of the proposed DDTMS againstother state-of-the-art schemes thereby requiring less time and fewer interactions for successfully identifying malevolentbehavior of compromised nodes.

Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We firstly propose a distributed state estimator assuming regular system operation, that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to (i) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, (ii) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and (iii) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies.

A Distributed Trust Management Scheme for Data Forwarding in Satellite DTN Emergency Communications

Satellite communications can be used when other communication systems are either destroyed or overloaded. Observation satellites and delay/disruption tolerant networks (DTNs) are technologies that can be interconnected to provide emergency communication for disaster recovery operations. DTNs use a store-carry-forward mechanism to forward messages through intermediary nodes to the destination node. The reliability of relaying messages through multi-hop nodes poses a significant problem in DTNs due to lack of consistent connectivity. These network characteristics make DTNs to heavily rely on the cooperation of neighboring nodes for the successful delivery of packets. However, the presence of malicious or selfish nodes will have a great impact on the network performance. In this paper, we design a decentralized trust management scheme (DTMS) to filter out malicious nodes in DTNs. First, the number of forwarding evidence is combined with the energy consumption rate of the nodes to formulate direct trust. Then, a recommendation trust is computed from the indirect trust, recommendation credibility, and recommendation familiarity. Recommendation credibility and familiarity improve the overall recommendation trust by filtering out dishonest recommendations. A comparative analysis of DTMS is performed against a cooperative watchdog scheme, recommendation based trust model, and spray and wait protocol. The results show that DTMS can effectively deal with malicious behaviors in DTNs including trust related attacks.

Establishing trust between energy aware nodes in MANETs: a family relationship-based approach

Mobile ad-hoc networks (MANETs) solely depends on cooperation between neighbour nodes in the network. The characteristics like topology changes, node mobility and node failure distinguish it from traditional networks and make it more vulnerable to attacks. Establishing trust between nodes plays a major role in MANETs as most of the attacks are performed by selfish and malicious nodes inside the network. There were many security schemes proposed based on cryptography, but all rely on centralised trust authority and pre-established infrastructure which is not possible in MANETs. Resource limitation parameters like energy constraint and signal strength will have to be considered in establishing trust. So we have proposed a family relationship routing protocol (FRRP) which assigns different relationship and privileges to nodes in the network based on direct and indirect trust by considering resource limitation parameters. This paper aims at providing decentralised and distributed trust management scheme to MANETs which does not depend on any predefined assumption.

Mitigating On-Off Attacks in the Internet of Things Using a Distributed Trust Management Scheme

In the Internet of Things (IoT), physical objects are able to provide or require determined services. The purpose of this work is to identify malicious behavior of nodes and prevent possible On-Off attacks to a multiservice IoT. The proposed trust management model uses direct information generated from direct communication with the nodes to evaluate trust between nodes. This distributed approach allows nodes to be completely autonomous in making decisions about the behavior of other nodes. We perform network simulations using Contiki-OS to analyze the performance of the proposed trust model. Simulation results show effectiveness against On-Off attacks and also a good performance to recognize malicious nodes in the network.

A novel multiple-level trust management framework for wireless sensor networks

The distributed deployment nature of wireless sensor networks (WSNs) poses a challenge to the security of node cooperation in them as it is difficult for WSN to ensure that all nodes can recognise a huge number of other individual nodes and select appropriate and trustworthy nodes for cooperation. Node cooperation may therefore be launched in an unreliable environment and might be vulnerable to attacks. Consequently, the security of nodes is of paramount importance for the proper operation of WSNs. The distributed trust management scheme is a feasible solution. With a view to making improvement on the existing trust management mechanisms, we in this paper propose ML-TRUST, a multiple-level trust management framework for trust management in WSN in which three levels of trust are used to establish trustworthy relationships among nodes for their cooperation, namely, (1) a subjective trust, which is defined as belief and is proposed with respect to three aspects: past judgements, witness evidence, and capacity evaluation; (2) an objective trust, which is defined as reputation and is proposed with two factors, number of functioning communities and weighted judgements by rating nodes’ reputations, being introduced in reputation rating, and with several rules and fraud factor tests being given to prevent reputation rating from malicious attacks, and (3) the recommended trust method, which is proposed to obtain trustable impressions from strange recommendations with, in connection, several consistency factors being presented to determine the trustworthiness of a recommendation. Besides using a set of lemmas and theorems to back up our ML-TRUST framework, we also list the results of a series of simulation tests to further verify the performance of our mechanism.