Challenges and Opportunities of Blockchain for Cyber Threat Intelligence Sharing

Dunnett, Kealan; Pal, Shantanu; Jadidi, Zahra

doi:10.1007/978-3-031-08270-2_1

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…Dunnett et al [10], presented their research about the challenges and opportunities of blockchain for CTI sharing. Nevertheless, their research was conducted in early 2022, which means it does not encompass the most recent developments in the field.…”

Section: Related Workmentioning

confidence: 99%

“…Dunnet et al [72] introduce a blockchain-based framework for sharing CTI that relies on trustless delegates for making trust-based decisions and decentralizing trust evaluation. This framework uses attribute-based encryption to achieve access control and allows CTI producers to periodically inject false data to monitor delegate behavior, enhancing transparency and accountability.…”

Section: All Blockchain Typesmentioning

confidence: 99%

“…Selective disclosure mechanisms inherent in blockchain, such as access controls, further contribute to privacy considerations, as this is highlighted in [46,58,64,67,72]. This feature allows for the controlled sharing of specific threat intelligence data, aligning with the need for granular control over information dissemination.…”

Section: Rq3: Research Focusmentioning

confidence: 99%

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Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Chatziamanetoglou,

Rantos

2024

Computers

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Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.

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Section: Related Workmentioning

confidence: 99%

Section: All Blockchain Typesmentioning

confidence: 99%