Cyber threat intelligence challenges: Leveraging blockchain intelligence with possible solution

Saxena, Rashi; Gayathri, E.

doi:10.1016/j.matpr.2021.06.204

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…Finally, Saxena et al [11] presented CTI challenges in a relationship with blockchain and presented a conceptual and abstract proposal for a blockchain-based CTI sharing model. However, their study, while insightful, does not comprehensively capture the entire landscape in the subject area of our research.…”

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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“…In addition to incident pattern identification, threat intelligence methods also aim to gather information on the attacker intentions, trends, and procedures, enabling the organization to better protect its informational assets [24]. However, the effective implementation of threat intelligence faces some challenges, such as the complexity of the threat landscape, the big data scenario, and the lack of transparency and visibility [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Data Breach from a Global Perspective: Incident Visualization and Data Protection Law Review

Pimenta Rodrigues,

Marques Serrano,

Lopes Espiñeira Lemos

et al. 2024

Data

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Data breaches result in data loss, including personal, health, and financial information that are crucial, sensitive, and private. The breach is a security incident in which personal and sensitive data are exposed to unauthorized individuals, with the potential to incur several privacy concerns. As an example, the French newspaper Le Figaro breached approximately 7.4 billion records that included full names, passwords, and e-mail and physical addresses. To reduce the likelihood and impact of such breaches, it is fundamental to strengthen the security efforts against this type of incident and, for that, it is first necessary to identify patterns of its occurrence, primarily related to the number of data records leaked, the affected geographical region, and its regulatory aspects. To advance the discussion in this regard, we study a dataset comprising 428 worldwide data breaches between 2018 and 2019, providing a visualization of the related statistics, such as the most affected countries, the predominant economic sector targeted in different countries, and the median number of records leaked per incident in different countries, regions, and sectors. We then discuss the data protection regulation in effect in each country comprised in the dataset, correlating key elements of the legislation with the statistical findings. As a result, we have identified an extensive disclosure of medical records in India and government data in Brazil in the time range. Based on the analysis and visualization, we find some interesting insights that researchers seldom focus on before, and it is apparent that the real dangers of data leaks are beyond the ordinary imagination. Finally, this paper contributes to the discussion regarding data protection laws and compliance regarding data breaches, supporting, for example, the decision process of data storage location in the cloud.

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“…An attack on the computer devices, network or system performed by person who has authorization access is known as an insider attack. An attack that originates exposures from outside the organization and attempt to exploit IT equipment are known as external attacks [1]. Cyber threat is possibility to successful cyber-attack that aims to harm computer system or network, steal sensitive data and gain unauthorized access.…”

Section: A Cyber Threatsmentioning

confidence: 99%

“…The term cyber generally, defines computer devices, network, internet and information technology. [1] Cyber threat is a possibility to successful cyber-attack that aims to harm computer system or network, steal sensitive data and gain unauthorized access. However, the IoT are vulnerable in terms of security; cybercriminals use malware attacks such as DDoS, ransomware, and IoT botnet attack to disable systems and networks.…”

Section: Introductionmentioning

confidence: 99%

Predicting Malicious Software in IoT Environment Based on Machine Learning and Data Mining Techniques

Alharbi¹,

Hamid²,

Lahza³

2022

IJACSA

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The Internet of Things (IoT) enable the IoT to sense and respond using the power of computing to autonomously come up with the best solutions for any industry today. However, Internet of Things have vulnerabilities since it can be hacked by cybercriminals. The cybercriminals know where the IoT vulnerabilities are, such as unsecured update mechanisms and malware (Malicious Software) to attack the IoT devices. The recently posted IoT-23 dataset based on several IoT devices such as Philips Hue, Amazon Echo devices and Somfy door lock were used for machine learning classification algorithms and data mining techniques with training and testing for predictive modelling of a variety of malware attacks like Distributed Denial of Service (DDoS), Command and Control (C&C) and various IoT botnets like Mirai and Okiru. This paper aims to develop predictive modeling that will predict malicious software to protect IoT and reduce vulnerabilities by using machine learning and data mining techniques. We collected, analyzed and processed benign and several of malicious software in IoT network traffic. Malware prediction is crucial in maintaining IoT devices' safety and security from cybercriminals' activities. Furthermore, the Principal Component Analysis (PCA) method was applied to determine the important features of IoT-23. In addition, this study compared with previous studies that used the IoT-23 dataset in terms of accuracy rate and other metrics. Experiments show that Random Forest (RF) classifier achieved the predictive model produced classification accuracy 0.9714% as well as predict 8754 samples with various types of malware and obtained 0.9644% of Area Under Curve (AUC) which outperforms several bassline machine learning classification models.

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Cyber threat intelligence challenges: Leveraging blockchain intelligence with possible solution

Cited by 23 publications

References 8 publications

Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Understanding Data Breach from a Global Perspective: Incident Visualization and Data Protection Law Review

Predicting Malicious Software in IoT Environment Based on Machine Learning and Data Mining Techniques

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