This shows you the differences between two versions of the page.
Both sides previous revision Previous revision | Next revision Both sides next revision | ||
security_economics [2018/11/26 00:37] fabio.massacci@unitn.it [Beyond 1-5 Risk Matrices: estimating quantitative attack success likelihood from data] |
security_economics [2018/11/26 00:38] fabio.massacci@unitn.it |
||
---|---|---|---|
Line 24: | Line 24: | ||
For a company, impact is easy to calculate as data about one's own asset is routinely collected. Likelihood is stillthe holy grail. So, both ISO/27001 and NIST 800-30 standards suggest the use of risk matrices as a tool to support such decisions. So you get a 5x5 risk matrix, where the interaction between the rare, frequent, ..., certain likelihood levels and the minor, severe, ..., critical consequence levels results in a final 5-level risk evaluation from low to high. This is pretty rough and well known to be full of errors. | For a company, impact is easy to calculate as data about one's own asset is routinely collected. Likelihood is stillthe holy grail. So, both ISO/27001 and NIST 800-30 standards suggest the use of risk matrices as a tool to support such decisions. So you get a 5x5 risk matrix, where the interaction between the rare, frequent, ..., certain likelihood levels and the minor, severe, ..., critical consequence levels results in a final 5-level risk evaluation from low to high. This is pretty rough and well known to be full of errors. | ||
- | In our {{:sp18.pdf|Risk Analysis paper}}. | + | In our {{allodi-risa-17.pdf|Risk Analysis paper}}. |
- | * L. Allodi, F. Massacci. **Security Events and Vulnerability Data for Cyber Security Risk Estimation.** To appear in //Risk Analysis// (Special Issue on Risk Analysis and Big Data), 2017.{{http://onlinelibrary.wiley.com/resolve/doi?DOI=10.1111/risa.12864|PDF at Publisher}}, {{http://www.win.tue.nl/~lallodi/allodi-risa-17.pdf|Authors' draft}} | ||
==== Cyber-Insurance: good for your company, bad for your country? ==== | ==== Cyber-Insurance: good for your company, bad for your country? ==== | ||
Line 226: | Line 225: | ||
===== Publications ===== | ===== Publications ===== | ||
+ | * L. Allodi, F. Massacci. **Security Events and Vulnerability Data for Cyber Security Risk Estimation.** To appear in //Risk Analysis// (Special Issue on Risk Analysis and Big Data), 2017.{{http://onlinelibrary.wiley.com/resolve/doi?DOI=10.1111/risa.12864|PDF at Publisher}}, {{http://www.win.tue.nl/~lallodi/allodi-risa-17.pdf|Authors' draft}} | ||
* F. Massacci, C.N. Ngo, J. Nie, D. Venturi, J. Williams. **The seconomics (security-economics) vulnerabilities of Decentralized Autonomous Organizations**. To appear in //Security Protocols Workshop (SPW)// 2017. {{https://drive.google.com/file/d/0By02ZB0MmV0ZeUM5clBBUHdNdms/view?usp=sharing|Author's Draft PDF}} | * F. Massacci, C.N. Ngo, J. Nie, D. Venturi, J. Williams. **The seconomics (security-economics) vulnerabilities of Decentralized Autonomous Organizations**. To appear in //Security Protocols Workshop (SPW)// 2017. {{https://drive.google.com/file/d/0By02ZB0MmV0ZeUM5clBBUHdNdms/view?usp=sharing|Author's Draft PDF}} | ||
* L. Allodi, F. Massacci, J. Williams. **The Work Averse Attacker Model.** In //Workshop on Economics of Information Security (WEIS)//, 2017. {{http://weis2017.econinfosec.org/wp-content/uploads/sites/3/2017/05/WEIS_2017_paper_13.pdf|PDF}} | * L. Allodi, F. Massacci, J. Williams. **The Work Averse Attacker Model.** In //Workshop on Economics of Information Security (WEIS)//, 2017. {{http://weis2017.econinfosec.org/wp-content/uploads/sites/3/2017/05/WEIS_2017_paper_13.pdf|PDF}} |