Risky business: Fine-grained data breach prediction using business profiles

Sarabi, Armin; Naghizadeh, Parinaz; Liu, Yang; Liu, Mingyan

doi:10.1093/cybsec/tyw004

Cited by 33 publications

(15 citation statements)

References 7 publications

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“…The sample of data breach incidents used in this study is drawn from the VCDB. Prior studies (Amir et al, 2018;Sarabi et al, 2016;Chernyshev et al, 2019) have used the VCDB to study data breaches in a variety of contexts. The VCDB is an open repository of publicly reported breach incidents developed by Verizon that standardizes how data breach incidents are recorded and reported.…”

Section: Data and Methodology Samplementioning

confidence: 99%

Data breaches in hospitality: is the industry different?

Gwebu

Barrows

2020

JHTT

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Purpose The purpose of this study is to expand on the existing literature by specifically examining data security incidents within the hospitality industry, assessing origins and causes, comparing breaches within the industry with those of other industries and identifying areas of concern. Design/methodology/approach A sample of data breach incidents is drawn from the Verizon VERIS Community Database (VCDB). Statistical comparisons between hospitality and non-hospitality industry firms are conducted following the Verizon A4 threat framework. Findings The results reveal that breaches between hospitality and non-hospitality firms differ significantly in terms of actors, actions, assets and attributes. Specifically, proportions of breaches in the hospitality industry are larger in terms of external actors, hacking and malware, user devices compromised and integrity violations. Additionally, compared to other industries, point-of-sales (POS) system breaches occur at a higher rate in the hospitality industry. The study finds that company size, hacking and malware predict the likelihood of a POS breach. Research limitations/implications The study uses secondary data and does not include the entire universe of data breaches. Originality/value In the quest to reduce data breach incidents, it is imperative to identify and assess the nature of data breach incidents between industries. Doing so permits the development of targeted industry-specific solutions rather than generic ones. This study systematically identifies differences between hospitality and non-hospitality data security incidents and then suggests areas where hospitality companies should focus future attention to mitigate breach incidents.

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Section: Data and Methodology Samplementioning

confidence: 99%

Data breaches in hospitality: is the industry different?

Gwebu

Barrows

2020

JHTT

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“…Predicting Security Incidents. Prior work studied the feasibility of predicting future computer-security incidents [34,42,51,69,71,72,76]. Soska and Christin showed that, using publicly available indicators, they could reliably predict whether websites would be compromised within one year [76].…”

Section: Related Workmentioning

confidence: 99%

Predicting Impending Exposure to Malicious Content from User Behavior

Sharif

Urakawa

Christin

et al. 2018

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

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Many computer-security defenses are reactive-they operate only when security incidents take place, or immediately thereafter. Recent efforts have attempted to predict security incidents before they occur, to enable defenders to proactively protect their devices and networks. These efforts have primarily focused on long-term predictions. We propose a system that enables proactive defenses at the level of a single browsing session. By observing user behavior, it can predict whether they will be exposed to malicious content on the web seconds before the moment of exposure, thus opening a window of opportunity for proactive defenses. We evaluate our system using three months' worth of HTTP traffic generated by 20,645 users of a large cellular provider in 2017 and show that it can be helpful, even when only very low false positive rates are acceptable, and despite the difficulty of making "on-the-fly" predictions. We also engage directly with the users through surveys asking them demographic and security-related questions, to evaluate the utility of self-reported data for predicting exposure to malicious content. We find that self-reported data can help forecast exposure risk over long periods of time. However, even on the long-term, self-reported data is not as crucial as behavioral measurements to accurately predict exposure. CCS CONCEPTS• Security and privacy → Network security; Human and societal aspects of security and privacy; • Computing methodologies → Neural networks; KEYWORDS Exposure prediction; network security; proactive security ACM Reference Format:

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“…There are several models in the literature related to forecasting incidents that can impact personal rights, such as the chance of cybersecurity breaches (Liu et al 2015, Sarabi et al 2016 or a proposal regarding litigation forecasting (Brown et al 2004). We focus on forecasting the number n p of personal identifiable information records exposed in an attack as a proxy.…”

Section: Personal Rightsmentioning

confidence: 99%

Assessing and Forecasting Cybersecurity Impacts

2020

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Cyberattacks constitute a major threat to most organizations. Beyond financial consequences, they may entail multiple impacts that need to be taken into account when making risk management decisions to allocate the required cybersecurity resources. Experts have traditionally focused on a technical perspective of the problem by considering impacts in relation with the confidentiality, integrity, and availability of information. We adopt a more comprehensive approach identifying a broader set of generic cybersecurity objectives, the corresponding set of attributes, and relevant forecasting and assessment models. These are used as basic ingredients for decision support in cybersecurity risk management.

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Risky business: Fine-grained data breach prediction using business profiles

Cited by 33 publications

References 7 publications

Data breaches in hospitality: is the industry different?

Data breaches in hospitality: is the industry different?

Predicting Impending Exposure to Malicious Content from User Behavior

Assessing and Forecasting Cybersecurity Impacts

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