AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks

In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.

SBTMS: Scalable Blockchain Trust Management System for VANET

With many advances in sensor technology and the Internet of Things, Vehicle Ad Hoc Network (VANET) is becoming a new generation. VANET’s current technical challenges are deploying decentralized architecture and protecting privacy. Because Blockchain features are decentralized, distributed, mass storage, and non-manipulation features, this paper designs a new decentralized architecture using Blockchain technology called Blockchain-based VANET. Blockchain-based VANET can effectively resolve centralized problems and mutual distrust between VANET units. To achieve this, it is needed to provide scalability on the blockchain to run for VANET. In this system, our focus is on the reliability of incoming messages on the network. Vehicles check the validity of the received messages using the proposed Bayesian formula for trust management system and some information saved in the Blockchain. Then, based on the validation result, the vehicle computes a rate for each message type and message source vehicle. Vehicles upload the computed rates to Roadside Units (RSUs) in order to calculate the net reliability value. Finally, RSUs using a sharding consensus mechanism generate blocks, including the net reliability value as a transaction. In this system, all RSUs collaboratively maintain the latest updated Blockchain. Our experimental results show that the proposed system is effective, scalable and dependable in data gathering, computing, organization, and retrieval of trust values in VANET.

Linear Regression Trust Management System for IoT Systems

This paper aims at creating a new Trust Management System (TMS) for a system of nodes. Various systems already exist which only use a simple function to calculate the trust value of a node. In the age of artificial intelligence the need for learning ability in an Internet of Things (IoT) system arises. Malicious nodes are a recurring issue and there still has not been a fully effective way to detect them beforehand. In IoT systems, a malicious node is detected after a transaction has occurred with the node. To this end, this paper explores how Artificial Intelligence (AI), and specifically Linear Regression (LR), could be utilised to predict a malicious node in order to minimise the damage in the IoT ecosystem. Moreover, the paper compares Linear regression over other AI-based TMS, showing the efficiency and efficacy of the method to predict and identify a malicious node.

Optimization of Wireless Sensor Network Architecture with Security System

Wireless sensor network (WSN) is a new type of wireless network. It has many advantages, but there are some problems. These problems make it easier for attackers to analyze network security holes and attack and destroy entire networks. This article designs a security wireless sensor network model. It can resist most known network attacks without significantly reducing the energy power of sensor nodes. First, we cluster the network organization to reduce energy consumption. It also protects the network based on the calculation of trust levels and the establishment of trust relationships between trusted nodes and operates the trust management system based on a centralized method, secondly, on the basis of LEACH agreement, draws lessons from the principle of biological immune system, optimizes the wireless sensor network, and further proposes a new immune system structure suitable for wireless sensor networks. The experimental results show that the wireless sensor network model designed in this paper solves the high-efficiency and energy-saving design task, and the trust management system has satisfactory results in defending against attacks.

Gateway based Trust Management System for Internet of Things

Internet of Things (IoT) a growing phenomenon, refers to the seamless integration of things into the information network. The security in IoT is tampered because of the various attacks which happen due to resource constrained nature of the devices in the network. Thus, although IoT is evolving as an attractive next generation networking paradigm, it can be adopted only when the security issues are resolved. This implies that, in a dynamic and collaborative IoT environment, the devices need to be trustworthy. This paper proposes a gateway based trust management system and an algorithm for computation of trust for the devices. The system focuses on making the computations on the devices lightweight and the network robust. The proposed system is tested against various IoT attacks and results demonstrate that it can clearly identify the malicious device if any, in the IoT network.

Hierarchical Hybrid Trust Management Scheme in SDN-Enabled VANETs

One of the principal missions of security in the Internet of Vehicles (IoV) is to establish credible social relationships. The trust management system has been proved to be an effective security solution in a connected vehicle environment. The use of trust management can play a significant role in achieving reliable data collection and dissemination and enhanced user security in the Internet of Vehicles. However, due to a large number of vehicles, the limited computing power of individuals, and the highly dynamic nature of the network, a universal and flexible architecture is required to realize the trust of vehicles in a dynamic environment. The existing solutions for trust management cannot be directly applied to the Internet of Vehicles. To ensure the safe transmission of data between vehicles and overcome the problems of high communication delay and low recognition rate of malicious nodes in the current trust management scheme, an efficient flow forwarding mechanism of the RSU close to the controller in the Software-Defined Vehicular Network is used to establish a hierarchical hybrid trust management architecture. This method evaluates the dynamic trust change of vehicle behavior based on the trust between vehicles and the auxiliary trust management of the infrastructure to the vehicle, combined with static and dynamic information and other indicators. The proposed trust management scheme is superior to the comparative schemes in resisting simple attacks, selective misbehavior attacks, and time-dependent attacks under the condition of ensuring superior real-time performance. Its overall accuracy is higher than that of the baseline scheme.

Targeted Context based Attacks on Trust Models in IoT Systems

Trust models play an important role in Internet of Things (IoT) as it provides a means of finding whether a given device can provide a service to a satisfactory level as well as a means for identifying potentially malicious devices in the network. Context awareness in trust models allows a trustor to filter and aggregate evidence by their relevance to the current situation. Context awareness is important in the formulation of trust in IoT networks due to their heterogeneity and due to the dynamic changes in the capabilities of IoT devices. In this paper,we have proposed a new type of context-based attack on context aware trust models for IoT systems. An adversary is able to manipulate the context and impact a target group of IoT devices, while other devices in non-targeted groups are not even aware of the attack. We have demonstrated the effectiveness of this new type of attack on six previously proposed trust models. Through practical simulations and theoretical proofs, we show that the adversaries can launch such context-based attacks against a targeted group of IoT devices in the network. The paper also proposes a new trust management system that can mitigate such context-based attacks.

Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model.

Targeted Context based Attacks on Trust Models in IoT Systems

Trust models play an important role in Internet of Things (IoT) as it provides a means of finding whether a given device can provide a service to a satisfactory level as well as a means for identifying potentially malicious devices in the network. Context awareness in trust models allows a trustor to filter and aggregate evidence by their relevance to the current situation. Context awareness is important in the formulation of trust in IoT networks due to their heterogeneity and due to the dynamic changes in the capabilities of IoT devices. In this paper,we have proposed a new type of context-based attack on context aware trust models for IoT systems. An adversary is able to manipulate the context and impact a target group of IoT devices, while other devices in non-targeted groups are not even aware of the attack. We have demonstrated the effectiveness of this new type of attack on six previously proposed trust models. Through practical simulations and theoretical proofs, we show that the adversaries can launch such context-based attacks against a targeted group of IoT devices in the network. The paper also proposes a new trust management system that can mitigate such context-based attacks.