Secure Incident &amp; Evidence Management Framework (SIEMF) for Internet of Vehicles using Deep Learning and Blockchain

Philip, Abin Oommen; Saravanaguru, R. A. K.

doi:10.1515/comp-2019-0022

Cited by 24 publications

(6 citation statements)

References 21 publications

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“…The prediction of violations committed by the driver is possible with the used of blockchain and learning. To predict the violation and traffic the deep learning algorithms utilize the highly secured blockchain data [163][164][165]. Such information can be immutably stored on the blockchain and utilized by the department of highways to develop the network of roads, insurance firms to evaluate the damage, and law enforcement organizations to implement legislation.…”

Section: Blockchain-based Deep Learning Servicesmentioning

confidence: 99%

WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

Afaq

Manocha

2022

Preprint

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Recently, Deep Learning and blockchain technology has gained successful attention in recent research due to their high potential for generating accurate decision. The operational transparency, traceability, dependability, security, and trustworthy data provenance characteristics are offered by the vast majority of current deep learning approaches that are developed on centralized servers. Therefore, using centralized data to train Deep Learning models makes them vulnerable to the single point of failure issue. In this article, the benefits of combining Blockchain and Deep Learning algorithms are explored. Moreover, the state-of-the-art literature is reviewed to find out the most possible way to integrate these two technologies for an optimal solution. Based on the literature survey this article is divided by a thematic taxonomy that includes seven different parameters such as Deep Learning techniques, protocols, domains, types of blockchain, use of deep learning and blockchain, and datasets. We have outlined the advantages and disadvantages of the most advanced blockchain-based deep learning frameworks to give insightful discussions. At last, the most important challenges that need further research are also highlighted with future recommendations.

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Section: Blockchain-based Deep Learning Servicesmentioning

confidence: 99%

WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

Afaq

Manocha

2022

Preprint

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“…In [44], A. Philip et al propose a framework for road accidents and traffic violations based on deep learning and blockchain. An accident warning system for vehicles is established by considering road and climate conditions and driving patterns as parameters.…”

Section: B Categorization Of Papersmentioning

confidence: 99%

A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection

Strandberg

Nowdehi

Olovsson

2023

IEEE Trans. Intell. Veh.

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A modern vehicle has a complex internal architecture and is wirelessly connected to the Internet, other vehicles, and the infrastructure. The risk of cyber attacks and other criminal incidents along with recent road accidents caused by autonomous vehicles calls for more research on automotive digital forensics. Failures in automated driving functions can be caused by hardware and software failures and cyber security issues. Thus, it is imperative to be able to determine and investigate the cause of these failures, something which requires trustable data. However, automotive digital forensics is a relatively new field for the automotive where most existing self-monitoring and diagnostic systems in vehicles only monitor safety-related events. To the best of our knowledge, our work is the first systematic literature review on the current research within this field. We identify and assess over 300 papers published between 2006 -2021 and further map the relevant papers to different categories based on identified focus areas to give a comprehensive overview of the forensics field and the related research activities. Moreover, we identify forensically relevant data from the literature, link the data to categories, and further map them to required security properties and potential stakeholders. Our categorization makes it easy for practitioners and researchers to quickly find relevant work within a particular sub-field of digital forensics. We believe our contributions can guide digital forensic investigations in automotive and similar areas, such as cyber-physical systems and smart cities, facilitate further research, and serve as a guideline for engineers implementing forensics mechanisms.

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“…Similarly, another framework known as MSecureChain employs decentralized authentication and access control and federated learning-based intrusion detection in a metaverse context for KDN smart devices, which establish trustworthy connections for communication [332]. Likewise, an evidence management system known as SIEMF for the internet of vehicles leverages deep learning to predict incident modeling while using self-executing contracts and attribute-based encryption to authorize entry and generate operations for permissioning rules in cases where granular access control has been effective due to blockchain technology [333]. Moreover, in [334], for a Knowledge-Defined Internet of Health Things Network, Support Vector Machines (SVMs) are integrated with blockchain and self-executing contracts for secure user identification, access control, and threat detection in order to transmit data to healthcare applications.…”

Section: Authentication Access Control and Encryptionmentioning

confidence: 99%

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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Secure Incident & Evidence Management Framework (SIEMF) for Internet of Vehicles using Deep Learning and Blockchain

Cited by 24 publications

References 21 publications

WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection

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

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