Abstract
Recent widely publicized data breaches have exposed the personal information of hundreds of millions of people. Some reports point to alarming increases in both the size and frequency of data breaches, spurring institutions around the world to address what appears to be a worsening situation. But, is the problem actually growing worse? In this article, we study a popular public dataset and develop Bayesian Generalized Linear Models to investigate trends in data breaches. Analysis of the model shows that neither size nor frequency of data breaches has increased over the past decade. We find that the increases that have attracted attention can be explained by the heavytailed statistical distributions underlying the dataset. Specifically, we find that the size of data breaches is well modeled by the log-normal family of distributions and that the daily frequency of breaches is described by a negative binomial distribution. These distributions may provide clues to the generative mechanisms that are responsible for the breaches. Additionally, our model predicts the likelihood of breaches of a particular size in the future. For example, we find that between 15 September 2015 and 16 September 2016 there is only a 53.6% chance of a breach of 10 million records or more in the USA. Regardless of any trend, data breaches are costly, and we combine the model with two different cost models to project that in the next 3 years breaches could cost up to $179 billion.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3-14 |
| Number of pages | 12 |
| Journal | Journal of Cybersecurity |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 1 2016 |
| Externally published | Yes |
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Keywords
- Bayesian linear model
- Data breaches
- Heavy tails
- Log-normal
- Negative binomial
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Networks and Communications
- Safety, Risk, Reliability and Quality
- Social Psychology
- Law
- Political Science and International Relations
Cite this
Hype and heavy tails : A closer look at data breaches. / Edwards, Benjamin; Hofmeyr, Steven; Forrest, Stephanie.
In: Journal of Cybersecurity, Vol. 2, No. 1, 01.12.2016, p. 3-14.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Hype and heavy tails
T2 - A closer look at data breaches
AU - Edwards, Benjamin
AU - Hofmeyr, Steven
AU - Forrest, Stephanie
PY - 2016/12/1
Y1 - 2016/12/1
N2 - Recent widely publicized data breaches have exposed the personal information of hundreds of millions of people. Some reports point to alarming increases in both the size and frequency of data breaches, spurring institutions around the world to address what appears to be a worsening situation. But, is the problem actually growing worse? In this article, we study a popular public dataset and develop Bayesian Generalized Linear Models to investigate trends in data breaches. Analysis of the model shows that neither size nor frequency of data breaches has increased over the past decade. We find that the increases that have attracted attention can be explained by the heavytailed statistical distributions underlying the dataset. Specifically, we find that the size of data breaches is well modeled by the log-normal family of distributions and that the daily frequency of breaches is described by a negative binomial distribution. These distributions may provide clues to the generative mechanisms that are responsible for the breaches. Additionally, our model predicts the likelihood of breaches of a particular size in the future. For example, we find that between 15 September 2015 and 16 September 2016 there is only a 53.6% chance of a breach of 10 million records or more in the USA. Regardless of any trend, data breaches are costly, and we combine the model with two different cost models to project that in the next 3 years breaches could cost up to $179 billion.
AB - Recent widely publicized data breaches have exposed the personal information of hundreds of millions of people. Some reports point to alarming increases in both the size and frequency of data breaches, spurring institutions around the world to address what appears to be a worsening situation. But, is the problem actually growing worse? In this article, we study a popular public dataset and develop Bayesian Generalized Linear Models to investigate trends in data breaches. Analysis of the model shows that neither size nor frequency of data breaches has increased over the past decade. We find that the increases that have attracted attention can be explained by the heavytailed statistical distributions underlying the dataset. Specifically, we find that the size of data breaches is well modeled by the log-normal family of distributions and that the daily frequency of breaches is described by a negative binomial distribution. These distributions may provide clues to the generative mechanisms that are responsible for the breaches. Additionally, our model predicts the likelihood of breaches of a particular size in the future. For example, we find that between 15 September 2015 and 16 September 2016 there is only a 53.6% chance of a breach of 10 million records or more in the USA. Regardless of any trend, data breaches are costly, and we combine the model with two different cost models to project that in the next 3 years breaches could cost up to $179 billion.
KW - Bayesian linear model
KW - Data breaches
KW - Heavy tails
KW - Log-normal
KW - Negative binomial
UR - http://www.scopus.com/inward/record.url?scp=85029531911&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029531911&partnerID=8YFLogxK
U2 - 10.1093/cybsec/tyw003
DO - 10.1093/cybsec/tyw003
M3 - Article
AN - SCOPUS:85029531911
VL - 2
SP - 3
EP - 14
JO - Journal of Cybersecurity
JF - Journal of Cybersecurity
SN - 2057-2093
IS - 1
ER -