A survey on cyber threat intelligence sharing based on Blockchain

El-Kosairy, Ahmed; Abdelbaki, Nashwa; Aslan, Heba K.

doi:10.1007/s43674-023-00057-z

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…El-Kosairy et al [9] conducted a survey on CTI sharing based on blockchain, collecting the latest research contributions that use blockchain to overcome the conventional CTI problems, comparing them for awareness about the different methods used, and pointing out uncovered areas for further research. It is important to note that this study presented the advancements in integrating CTI with blockchain technology, emphasizing pertinent challenges within the domain.…”

Section: Related Workmentioning

confidence: 99%

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%

Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Chatziamanetoglou,

Rantos

2024

Computers

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Automating shareable cyber threat intelligence production for closed source software vulnerabilities: a deep learning based detection system

Arıkan,

Koçak,

Alkan

2024

Int. J. Inf. Secur.

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Software can be vulnerable to various types of interference. The production of cyber threat intelligence for closed source software requires significant effort, experience, and many manual steps. The objective of this study is to automate the process of producing cyber threat intelligence, focusing on closed source software vulnerabilities. To achieve our goal, we have developed a system called cti-for-css. Deep learning algorithms were used for detection. To simplify data representation and reduce pre-processing workload, the study proposes the function-as-sentence approach. The MLP, OneDNN, LSTM, and Bi-LSTM algorithms were trained using this approach with the SOSP and NDSS18 binary datasets, and their results were compared. The aforementioned datasets contain buffer error vulnerabilities (CWE-119) and resource management error vulnerabilities (CWE-399). Our results are as successful as the studies in the literature. The system achieved the best performance using Bi-LSTM, with F1 score of 82.4%. Additionally, AUC score of 93.0% was acquired, which is the best in the literature. The study concluded by producing cyber threat intelligence using closed source software. Shareable intelligence was produced in an average of 0.1 s, excluding the detection process. Each record, which was represented using our approach, was classified in under 0.32 s on average.

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