Deep Q-Learning on Internet of Things System for Trust Management in Multi-Agent Environments for Smart City

Smart Cities are vital to improving urban efficiency and citizen quality of life due to the fast rise of the Internet of Things (IoT) and its integration into varied applications. Smart Cities are dynamic and complicated, making trust management in multi-agent systems difficult. Trust helps IoT devices and agents in smart ecosystems connect and cooperate. This study suggests using Deep Q-Learning and Bidirectional Long Short-Term Memory (Bi-LSTM) to manage trust in multi-agent Smart City settings. Deep Q-Learning and Bi-LSTM represent long-term relationships and temporal dynamics in the IoT network, enabling intelligent trust-related judgments. The architecture supports real-time trust assessment, decision-making, and response to smart city changes. The suggested solution improves dependability, security, and trustworthiness in the IoT system's networked agents. A complete collection of studies utilizing real-world IoT data from a simulated Smart City evaluates the system's performance. The Deep Q-Learning and Bi-LSTM technique surpasses existing trust management approaches in dynamic, complicated multi-agent environments. The system's capacity to adapt to changing situations and improve decision-making make IoT device interactions more dependable and trustworthy, helping Smart Cities expand sustainably and efficiently

Decentralized self-enforcing trust management system for social Internet of Things

The Internet of Things (IoT) is the network of connected computing devices that have the ability to transfer valued data between each other via the Internet without requiring human intervention. In such a connected environment, the social IoT (SIoT) has become an emerging trend where multiple IoT devices owned by users support communication within a social circle. Trust management in the SIoT network is imperative as trusting the information from compromised devices could lead to serious compromises within the network. It is important to have a mechanism where the devices and their users evaluate the trustworthiness of other devices and users before trusting the information sent by them. The privacy preservation, decentralization, and self-enforcing management without involving trusted third parties are the fundamental challenges in designing a trust management system for SIoT. To fulfill these challenges, this article presents a novel framework for computing and updating the trustworthiness of participants in the SIoT network in a self-enforcing manner without relying on any trusted third party. The privacy of the participants in the SIoT is protected by using homomorphic encryption in the decentralized setting. To achieve the properties of self-enforcement, the trust score of each device is automatically updated based on its previous trust score and the up-to-date tally of the votes by its peers in the network with zero-knowledge proofs (ZKPs) to enforce that every participant follows the protocol honestly. We evaluate the performance of the proposed scheme and present evaluation benchmarks by prototyping the main functionality of the system. The performance results show that the system has a linear increase in computation and communication overheads with more participants in the network. Furthermore, we prove the correctness, privacy, and security of the proposed system under a malicious adversarial model

Secured information dissemination and misbehavior detection in VANETs

In a connected vehicle environment, the vehicles in a region can form a distributed network (Vehicular Ad-hoc Network or VANETs) where they can share traffic-related information such as congestion or no-congestion with other vehicles within its proximity, or with a centralized entity via. the roadside units (RSUs). However, false or fabricated information injected by an attacker (or a malicious vehicle) within the network can disrupt the decision-making process of surrounding vehicles or any traffic-monitoring system. Since in VANETs the size of the distributed network constituting the vehicles can be small, it is not difficult for an attacker to propagate an attack across multiple vehicles within the network. Under such circumstances, it is difficult for any traffic monitoring organization to recognize the traffic scenario of the region of interest (ROI). Furthermore, even if we are able to establish a secured connected vehicle environment, an attacker can leverage the connectivity of individual vehicles to the outside world to detect vulnerabilities, and disrupt the normal functioning of the in-vehicle networks of individual vehicles formed by the different sensors and actuators through remote injection attacks (such as Denial of Service (DoS)). Along this direction, the core contribution of our research is directed towards secured data dissemination, detection of malicious vehicles as well as false and fabricated information within the network. as well as securing the in-vehicle networks through improvisation of the existing arbitration mechanism which otherwise leads to Denial of Service (DoS) attacks (preventing legitimate components from exchanging messages in a timely manner). --Abstract, page iv

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

A Trust Management Framework for Vehicular Ad Hoc Networks

The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers