arXiv subjects

Armin Sarabi

Publications and source records attributed to Armin Sarabi.

3 recordsLinked to original sources

arXiv2026

Estimating the Social Cost of Corporate Data Breaches

While the size of a data breach is typically measured by the number of (consumer, customer, or user) records exposed or compromised, its economic impact is generally measured from the point of view of the corporation suffering the data breach: cost in crisis management, legal fees, drop in stock price, and so on. This study examines whether it is possible to estimate the true cost, or the social cost of a data breach, measured by the impact on its victims and their out of pocket costs. To accomplish this we establish: (1) the estimation of the average direct financial losses of an identity theft (IDT) victim, including the opportunity cost of lost time, and healthcare expenditures associated with distress associated with identity theft; and (2) the estimation of increases in incidents of IDT that can be attributed to a major breach event. Our findings show that the average social cost per victim has declined significantly since 2016. Furthermore, we find that there is indeed a statistically significant increase in the number of IDTs following a mega-breach event when accounting for a discovery lag of 1-2 months post-breach. Applying our model to real-world cases allows us to estimate an upper and lower bound social cost of specific mega-breach events. We find that for the 2009 Heartland and 2013 Target breaches, even the conservative lower bound social cost estimate exceeded settlements by factors of 5 and 18, respectively. In contrast, the 2017 Equifax breach resulted in a lower bound estimate of $263.8 million, falling well within its $700 million settlement cap. While the Equifax upper bound estimate of $1.72 billion in social cost more than doubles this settlement, the narrowing gap between institutional liability and an incident's social cost provides empirical evidence of a market saturation effect that reduces the marginal damage of individual compromised records over time.

cs.CR↗

arXiv2025

Scoring the Unscorables: Cyber Risk Assessment Beyond Internet Scans

In this paper we present a study on using novel data types to perform cyber risk quantification by estimating the likelihood of a data breach. We demonstrate that it is feasible to build a highly accurate cyber risk assessment model using public and readily available technology signatures obtained from crawling an organization's website. This approach overcomes the limitations of previous similar approaches that relied on large-scale IP address based scanning data, which suffers from incomplete/missing IP address mappings as well as the lack of such data for large numbers of small and medium-sized organizations (SMEs). In comparison to scan data, technology digital signature data is more readily available for millions of SMEs. Our study shows that there is a strong relationship between these technology signatures and an organization's cybersecurity posture. In cross-validating our model using different cyber incident datasets, we also highlight the key differences between ransomware attack victims and the larger population of cyber incident and data breach victims.

cs.CR↗

arXiv2023

Enhancing Vulnerability Prioritization: Data-Driven Exploit Predictions with Community-Driven Insights

The number of disclosed vulnerabilities has been steadily increasing over the years. At the same time, organizations face significant challenges patching their systems, leading to a need to prioritize vulnerability remediation in order to reduce the risk of attacks. Unfortunately, existing vulnerability scoring systems are either vendor-specific, proprietary, or are only commercially available. Moreover, these and other prioritization strategies based on vulnerability severity are poor predictors of actual vulnerability exploitation because they do not incorporate new information that might impact the likelihood of exploitation. In this paper we present the efforts behind building a Special Interest Group (SIG) that seeks to develop a completely data-driven exploit scoring system that produces scores for all known vulnerabilities, that is freely available, and which adapts to new information. The Exploit Prediction Scoring System (EPSS) SIG consists of more than 170 experts from around the world and across all industries, providing crowd-sourced expertise and feedback. Based on these collective insights, we describe the design decisions and trade-offs that lead to the development of the next version of EPSS. This new machine learning model provides an 82\% performance improvement over past models in distinguishing vulnerabilities that are exploited in the wild and thus may be prioritized for remediation.

cs.CR↗