UASTrustChain: A Decentralized Blockchain- Based Trust Monitoring Framework for Autonomous Unmanned Aerial Systems

Keshavarz, Mahsa; Gharib, Morteza; Afghah, Fatemeh; Ashdown, Jonathan

doi:10.1109/access.2020.3044844

Cited by 15 publications

(8 citation statements)

References 31 publications

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“…Multicast teams are established for these individuals on the public blockchain [124]. UASTrustChain is a trust management framework for UAV networks where observers maintain the trust score of UAVs in a secure and reliable blockchain ledger to detect abnormal behaviors [125]. Moreover, the integration of blockchain and the Internet of Things (IoT) has resulted in a new paradigm called Blockchain and the Internet of Things (BIoT), in which there are intellectual cores such as data security and privacy, applications, frameworks, etc.…”

Section: Networkingmentioning

confidence: 99%

“…Hashing [82], Public key cryptography [80], Zero-knowledge proofs [84], Post-quantum cryptography [87,88,90], Hybrid cryptography [86,89], fault-tolerant ciphers [91][92][93][94][95] Endpoint attacks 51% vulnerability, cryptojacking [143] Application Smart contracts Contractual automation [116], access control [117] Cryptocurrency Bitcoin [2], Ethereum [81] Supply chain Cold chain management [121] Networking TRUCON [123], IoTChain [124], UASTrustChain [125] Healthcare Drug counterfeiting [127], Medblock [128], MedShare [129] Insurance ClaimChain [130], Insurance fraud protection [131], Decentralized reinsurance [132] Voting PriScore [133], Auditable voting [134] Intellectual property IP protection [135], IP rights management [136] 3. Synopsis of KDN Paradigm…”

Section: Consortium Blockchainmentioning

confidence: 99%

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A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Wijesekara,

Gunawardena

2023

Network

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Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.

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Section: Networkingmentioning

confidence: 99%

Section: Consortium Blockchainmentioning

confidence: 99%

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Wijesekara,

Gunawardena

2023

Network

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“…Numerous algorithms and analytical models explored in the literature can mitigate these software attacks, but their performance varies depending upon the system models and the types of threats. Table 5 summarizes recent research works [86], [87], [88], [89], [90], [91], [92], [93] carried in the field of secrecy improvement and cyber attack analysis.…”

Section: F Software Installation Threatsmentioning

confidence: 99%

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

et al. 2022

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Unmanned aerial vehicles (UAVs) have been instrumental in enabling many new applications and services, including military and rescue operations, aerial surveillance, civilian applications, precision farming, as well as providing extensive wireless network access in remote areas. They have also been used in other areas such as transmission line and oil rig monitoring, disaster recovery, etc. With their increasing payload ability and flight duration, using UAVs is a preferred choice for a multitude of upcoming wireless communication systems. However, because of their open operational nature, UAVs are highly vulnerable to severe security breaches via cyber attacks, eavesdropping on navigational and communication links, etc. Given their widespread applications, the requirement of secured UAV communications has become more and more critical since security failures can lead to detrimental consequences. Several works have examined the extent of privacy and security issues in UAV-assisted networks and introduced various mitigation techniques to address different security challenges. In this paper, a comprehensive survey is conducted that centers on the security issues related to UAV-aided networks. A descriptive taxonomy of various security intrusions on the UAV networks and commonly-used secrecy performance metrics are thoroughly reviewed. An in-depth discussion is provided on alleviating threats with proactive security techniques blended with key wireless communication technologies such as mmWave, non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and cognitive radio. Moreover, several emerging topics like machine learning, software-defined networks, fog and edge computing, blockchain, are discussed in the context of UAV-aided secure communications.INDEX TERMS Physical layer security, unmanned aerial vehicles (UAVs), proactive mitigation techniques, secrecy metrics, emerging wireless technologies.GAURAV KUMAR PANDEY (Graduate Student Member, IEEE) received the B.Tech. degree in electrical and electronics engineering from the

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“…To prevent data tampering between RSUs and malicious vehicles, the system selects the miners who generate the blocks through voting among all vehicles. In the context of trust management, the work presented in [27] introduces a trust management framework based on a series of blockchain timestamps, where the behavior of these unmanned aerial vehicles is periodically monitored by a group of distributed observers (DOs). DOs calculate relative trust scores for each UAV and record these scores in a transparent and secure ledger.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes

Zuo,

Cao,

et al. 2023

Drones

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In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.

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UASTrustChain: A Decentralized Blockchain- Based Trust Monitoring Framework for Autonomous Unmanned Aerial Systems

Cited by 15 publications

References 31 publications

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes

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