A Probabilistic Analysis of Cyber Risks

Patè‐Cornell, M. Elisabeth; Kuypers, Marshall

doi:10.1109/tem.2020.3028526

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…In order to assist investors in making decisions about their investments in a variety of different situations, a methodology for quantitative risk analysis is proposed in [6]. Data collection is difficult because external researchers and organizations sometimes lack a fundamental grasp of their systems.…”

Section: Related Workmentioning

confidence: 99%

Threats, Effects, and Awareness of Cybercrime: A Survey

Arora

2024

IJSREM

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The emergence of the digital age has ushered in unprecedented connectivity and convenience, but it has also brought about a surge in cybercrimes, posing significant challenges to security. Criminals exploit vulnerabilities in cyberspace to access sensitive information, perpetrate fraudulent activities, and undermine trust in online interactions. Despite the pervasive impact of cyber threats on various facets of society, awareness levels among the general populace remain a critical concern. This paper presents findings from a comprehensive survey aimed at assessing public awareness of cybercrimes and cybersecurity measures. The survey, conducted through a structured questionnaire, solicited responses from a diverse demographic sample, encompassing individuals' background information and their perceptions of cyber threats. Participants were queried on their familiarity with prevalent cyber scams, including phishing, email spoofing, lottery scams, credit card frauds, investment scams, vishing frauds, and gaming frauds. Key findings reveal gaps in awareness and understanding of cyber risks, underscoring the need for enhanced education and vigilance. Furthermore, insights into participants' responses regarding their experiences with cybercrimes, including measures taken to address incidents and financial losses incurred, shed light on the real-world impact of cyber threats. In conclusion, this paper emphasizes the importance of proactive measures to safeguard against cybercrimes and offers recommendations for bolstering cybersecurity awareness and resilience in the digital age. Key Words: Cybercrime, Scams, threats, Analysis, Awareness.

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Section: Related Workmentioning

confidence: 99%

Threats, Effects, and Awareness of Cybercrime: A Survey

Arora

2024

IJSREM

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“…From a high-level perspective, we note that this problem is not too different from what social media faces, except that, given the tendency to use structured formats, CTI is an easier problem. Detection of duplicates, for example, seems not to be a hard problem from simple comparison field-by-field, or by detecting keywords or named entities [74], to using simple techniques in machine learning [95] or taking advantage of data-driven or Big Data methods to assert relevance from high volumes of data [87], [97], [98]. Another alternative, complementary, is to design architectures with native ranking mechanisms [57], [95], [99].…”

Section: ) Architectures and Trust Managementmentioning

confidence: 99%

Sharing Is Caring: Hurdles and Prospects of Open, Crowd-Sourced Cyber Threat Intelligence

Jesus,

Bains,

Chang

2024

IEEE Trans. Eng. Manage.

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Cyber threat intelligence (CTI) is widely recognized as an important area in cybersecurity but it remains an area showing silos and reserved for large organizations. For an area whose strength is in open and responsive sharing, we see that the generation of feeds has a small scale, is secretive, and is nearly always from specialized businesses that have a commercial interest in not publicly sharing insights at a speed where it could be effective in raising preparedness or stopping an attack. This article has three purposes. First, we extensively review the state and challenges of open, crowd-sourced CTI, with a focus on the perceived barriers. Second, having identified that confidentiality (in multiple forms) is a key barrier, we perform a confidentiality threat analysis of existing sharing architectures and standards, including reviewing circa one million of real-world feeds between 2014 and 2022 from the popular open platform MISP toward quantifying the inherent risks. Our goal is to build the case that, either by redesigning sharing architectures or simply performing simple sanitization of shared information, the confidentiality argument is not as strong as one may have presumed. Third, after identifying key requirements for open crowd-based sharing of CTI, we propose a reference (meta-) architecture.Managerial Relevance-CTI is widely recognized as a key advantage toward cyber resilience in its multiple dimensions, from business continuity to reputation/regulatory protection. Furthermore, as we review in this article, there are strong indications that the next generation of approaches to cybersecurity will be centered on CTI. Whereas CTI is an established business area, we see little adoption, closed communities, or high costs that small businesses cannot afford. For an area that, intuitively, should be open, as velocity and accuracy of information is crucial, we shed light on why we have no significant open, crowd-sourced CTI. In other words, why is usage so lacking? We identify reasons and deconstruct unclear and unhelpful rationales by looking at a wide range of literature (research and professional) and an analysis of nearly ten years of open CTI data. Our findings from current data indicate two types of reasons. One, and dominant, is unhelpful perceptions (e.g., confidentiality), and another stems from market factors (e.g., "free-riding") that need collective movement as no single player may be able to break the cycle. After looking at motivations and barriers, we review existing technologies, elicit requirements, and propose a high-level open CTI sharing architecture that could be used as a reference for practitioners.

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“…Econometric modeling solves economic problems by selecting appropriate economic variables and model structure, building, solving and testing this model under a certain economic theory, and later giving economic explanations. The test of a classical linear econometric model must pass four levels of test gates, i.e., economic significance test, statistical test, econometric test, and prediction test [20][21][22]. In practical modeling, it is difficult to perform a comprehensive econometric model test, and depending on economic theory or experience, it is sometimes necessary to examine whether the model parameters satisfy certain a priori assumptions, i.e., constraints.…”

Section: Wind Control Model Constructed Based On An Econometric Modelmentioning

confidence: 99%

Research on risk prediction and prevention and control measures of economic management of engineering construction based on econometric model

Yang,

Li,

Yang

2023

Applied Mathematics and Nonlinear Sciences

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To provide construction units with comprehensive and accurate information to assist in decision-making, this paper utilizes econometric models to use the Internet for management monitoring and engineering construction schedule scheduling. The parametric linearity constraint test method is set up, production function examples are established for engineering tests, and statistics are approximated to obey degrees of freedom and cardinal distribution. To obtain the inverse of the estimated information matrix, the least squares method is employed to maximize the engineering construction expression. The engineering economic management error value is found to be the lowest value of 0.84, the maximum value of elasticity coefficient is 0.379, and the minimum value of covariance is 0.185. To fully improve the risk prediction capability of engineering construction, econometric modeling techniques must be integrated.

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A Probabilistic Analysis of Cyber Risks

Cited by 12 publications

References 32 publications

Threats, Effects, and Awareness of Cybercrime: A Survey

Threats, Effects, and Awareness of Cybercrime: A Survey

Sharing Is Caring: Hurdles and Prospects of Open, Crowd-Sourced Cyber Threat Intelligence

Research on risk prediction and prevention and control measures of economic management of engineering construction based on econometric model

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