Enhancing Reactive Ad Hoc Routing Protocols with Trust

In wireless ad hoc networks, security and communication challenges are frequently addressed by deploying a trust mechanism. A number of approaches for evaluating trust of ad hoc network nodes have been proposed, including the one that uses neural networks. We proposed to use packet delivery ratios as input to the neural network. In this article, we present a new method, called TARA (Trust-Aware Reactive Ad Hoc routing), to incorporate node trusts into reactive ad hoc routing protocols. The novelty of the TARA method is that it does not require changes to the routing protocol itself. Instead, it influences the routing choice from outside by delaying the route request messages of untrusted nodes. The performance of the method was evaluated on the use case of sensor nodes sending data to a sink node. The experiments showed that the method improves the packet delivery ratio in the network by about 70%. Performance analysis of the TARA method provided recommendations for its application in a particular ad hoc network.

Research on the Trust Mechanism of Individual Consumers in Rural Financial Markets Based on the Dynamic CGE Model

In order to obtain the complete equilibrium state of rural financial market and ensure the stable development of rural financial consumer market, this paper introduces CGE model and analyzes the dynamic trust mechanism of individual consumers in rural financial market. In this paper, the single variable evolutionary fuzzy clustering algorithm is used to analyze the orthogonal eigenvector solutions of individual consumers; the big data of individual consumers under the mode of perceived trust is automatically clustered, so as to obtain the fuzzy analogy function of individual consumers in the rural financial market; and finally the prediction value of consumer trust is obtained. The results show that trust, customer satisfaction, and service quality are positively correlated. Under the same sample expectation constraints, the dynamic CGE model is more robust, and the individual consumer trust mechanism of rural financial market in the study area has higher advantages.

An optimized deep learning-based trust mechanism In VANET for selfish node detection

Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance. Findings A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead. Practical implications The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks. Originality/value This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

Preventing Cognitive User Emulation Attack in Cognitive Radio Network by Calculating Trust Values Using Fuzzy Logic

Cognitive radio has proved to be an efficient and promising technology for the future of wireless networks. Its major and fundamental aim is to utilize the spectrum bands which are not efficiently exercised. These bands can be accessed using Opportunistic Spectrum Access (OSA), by a secondary user only when primary user is not transmitting over the channel. Cognitive radio manages spectrum through its cognitive radio cycle, which performs a set of management functions such as, spectrum sensing, spectrum assignment, spectrum sharing and spectrum mobility/handoff. During this cycle, at several stages, cognitive radio is very much vulnerable to security attacks. This is also due to the exposed nature of cognitive radio architecture. One such security attack which has not been much explored and can cause serious security issues is Cognitive User Emulation Attack (CUEA). This attack is expected to occur at the time of spectrum handoff. In this article the reason of occurrence of CUEA is explained along with counter measures to prevent this threat in the network by implementing trust mechanism using fuzzy logic. The proposed system is simulated and analyzed using MATLAB tool.

Modernisation of urban governance: An approach of ‘Blockchain + Big Data’

Mathematics is a prerequisite for the development of blockchain technology. The deeply penetrated mathematical ideas support the establishment of the trust mechanism of the whole blockchain system, which makes the blockchain technology autonomous, decentralised, not so easy to tamper, open, anonymous and also possesses other characteristics. Due to these characteristics, the introduction of blockchain will greatly solve a series of problems faced by the quality and acquisition of big data in cities, and release more data vitality. Based on the perspective of chain blocks and big data fusion, this paper puts forward that data are the foundation of modern urban governance. Data management has become the key to modern urban governance. It puts forward that the building of a big data management system based on blockchain will strengthen the construction of the intelligent city and modernisation of urban governance capabilities.

Correlation and trust mechanisms-based rumor propagation model in complex social networks

In real life, the process of rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagation model, in this paper, we take some psychological factors into consideration to mirror rumor propagation. Firstly, we use the Ridenour model to combine the trust mechanism with the correlation mechanism and propose a modified rumor propagation model. Secondly, mean-field equations which describe the dynamics of the modified SIR model on homogenous and heterogeneous networks are derived. Thirdly, a steady-state analysis is conducted for the spreading threshold and the final rumor size. Fourthly, we investigate rumor immunization strategies and obtain immunization thresholds. Next, simulations on different networks are carried out to verify the theoretical results and the effectiveness of the immunization strategies. The results indicate that the utilization of trust and correlation mechanisms leads to a larger final rumor size and a smaller terminal time. Moreover, different immunization strategies have disparate effectiveness in rumor propagation.

Research on Security Communication Technology of Internet of Vehicles Based on 5G Technology

With the improvement of the economic level and the development of infrastructure, the use of automobiles as a mode of transportation has become more and more common, and the number of cars has continued to grow. Based on this background, the rapid development of intelligent and networked automotive network communication technology, the birth of the Internet of Vehicles technology, has promoted the transformation of the automotive industry. This article gives the corresponding security requirements based on the security risks of IoV communication, briefly introduces the basic methods to realize the security of IoV communication, and proposes the service architecture, PKI architecture and multi-PKI system mutual trust mechanism to realize the security of IoV communication based on this.

DC-LTM: A Data Collection Strategy Based on Layered Trust Mechanism for IoT

A large number of Internet of Things (IoT) devices such as sensor nodes are deployed in various urban infrastructures to monitor surrounding information. However, it is still a challenging issue to collect data in a low-cost, high-quality, and reliable manner through IoT technique. Although the recruitment of mobile vehicles (MVs) to collect urban data has proved to be an effective method, most existing data collection systems lack a trust detection mechanism for malicious terminal nodes and malicious vehicles, which should lead to security vulnerabilities in practice. This paper proposes a novel data collection strategy based on a layered trust mechanism (DC-LTM). The strategy recruits MVs as data collectors of the sensor nodes based on the data value in the city, evaluates the trustworthiness of the data reported by the nodes, and records the results to the cloud data center. Furthermore, in order to make the data collection system more efficient and trust mechanism more reliable, we introduce unmanned aerial vehicles (UAVs) dispatched by data centers to actively verify the core sensor node data and use the core sensor data as baseline data to evaluate the credibility of the vehicles and the trust value of the whole network sensor nodes. Different from the previous strategies, UAVs adopts the DC-LTM method to obtain the node data while actively obtaining the trust value of MVs and nodes, which effectively improves the quality of data acquisition. Simulation results show that the mechanism effectively distinguishes malicious vehicles that provide false data in exchange for payment and reduces the total cost of system recruitment payments. At the same time, the proposed incentive mechanism encourages vehicle to complete the evaluation task and improves the accuracy of node trust evaluation. The recognition rates of false data attacks and flooding attacks as well as the recognition error rate of normal nodes are 100%, 98.9%, and 3.9%, respectively, which improves the quality of system data collection as a whole.