{"id":1300,"date":"2012-06-16T12:13:20","date_gmt":"2012-06-16T17:13:20","guid":{"rendered":"http:\/\/rud.is\/b\/?p=1300"},"modified":"2018-03-10T08:01:38","modified_gmt":"2018-03-10T13:01:38","slug":"weis-2012-best-of-the-best","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2012\/06\/16\/weis-2012-best-of-the-best\/","title":{"rendered":"WEIS 2012 :: Best of the Best"},"content":{"rendered":"

Sadly, I could not make it to this year’s Workshop on the Economics of Information Security<\/a>. However, the intrepid conference organizers were quick to post the papers<\/a> that were presented, and I had a chance to sift through them to pick out what I believe to be the best of the best (they are all worth reading).<\/p>\n

A Focus On The Bottom Line<\/b><\/p>\n

First up is “Measuring the Cost of Cybercrime<\/a>” by Ross Anderson, Chris Barton, Rainer B \u0308ohme, Richard Clayton, Michel J.G. van Eeten, Michael Levi, Tyler Moore & Stefan Savage. They developed an interesting framework:<\/p>\n

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which tries to cover all angles of loss (including costs of defense) as well as that of gain by the criminals. They don’t just talk theory & math. They did actual investigations and have produced a great breakdown of costs & criminal gains on page 24 of the paper (click for larger image<\/i>):<\/p>\n

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Beyond the details of their methodology, I include them in this list \u2013 in part \u2013\u00a0because of this paragraph:<\/p>\n

The straightforward conclusion to draw on the basis of the comparative figures collected in this study is that we should perhaps spend less in anticipation of computer crime (on antivirus, firewalls etc.) but we should certainly spend an awful lot more on catching and punishing the perpetrators.<\/i><\/p><\/blockquote>\n

What a great, data-backed discussion-starter at your next security conference!<\/p>\n

Might As Well Jump<\/b><\/p>\n

Next up is a very maths-y offering by Adrian Baldwin, Iffat Gheyas, Christos Ioannidis, David Pym & Julian Williams on “Contagion in Cybersecurity Attacks<\/a>“.<\/p>\n

If you’re put off by math symbols, jump to the bottom of page four to stat your reading (right after reading the abstract & introduction). The authors used DShield<\/a> data and focused on ten services (DNS, ssh, Oracle [they got the port #’s wrong], SQL, LDAP, http\/s, SMB, IMAP\/S, SMTP) sampled daily for the period 1 January 2003 to 28 February 2011. You can read the paper for their particular findings in this data set, but this extract hones in on the utility of their methodology:<\/p>\n

Security threats to data, its quality and accessibility, represent potential losses to the integrity of the operations of the organization. Security managers, in assessing the potential risks, should be interested in the relationship between the contagious threats to these different security attributes. The nature of the inter- relationship between the threats provides additional information to assist managers in making their choices of mitigating responses. For example, if the inter-relationship between threats is constant, independently of the frequency and intensity of threats, security managers can adopt smooth mitigation profiles to meet the threat. In the absence of such stable relationships, the managers\u2019 responses must be adjusted dynamically: for given temporal relationships between the number of attacks, their change (or \u2018jump\u2019) in frequency, and their change in size (extent of impact).<\/i><\/p><\/blockquote>\n

I can envision some product extensions incorporating this threat analysis into their offering or even service providers such as Akamai (they have deep, active threat intel) creating a broad, anonymized “contagion” report for public consumption with private, direct (paid) offerings for their clients.<\/p>\n

That Is The Question<\/b><\/p>\n

Lukas Demetz & Daniel Bachlechner hope to help security managers choose investment analysis strategies in their work on “To invest or not to invest?
\nAssessing the economic viability of a policy and security configuration management tool<\/a>“. They take eleven economic investment models and work through each of them for a selected tool\/technology investment, pointing out the strengths & weaknesses of each (click for larger version of the summary table<\/i>):<\/p>\n

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Unsurprisingly (at least for me), none were optimal, but this is the perfect paper for anyone who ever wanted to look at a summary\/overview of the “should we invest?” work with an eye on real practicality.<\/p>\n

Physician, Secure Thy Data<\/b><\/p>\n

Martin S. Gaynor, Muhammad Zia Hydari & Rahul Telang aim to assess the impact of market competition on information security and privacy in their work on “Is Patient Data Better Protected in Competitive Healthcare Markets?<\/a>“.<\/p>\n

I first have to hand it to these researches for including the “WORK IN PROGRESS – PLEASE DO NOT QUOTE” tag right up front in the paper. Our industry seems to be one to jump on “facts” way to soon and this should give any infosec pundits pause.<\/p>\n

However, (myself ignoring that previous paragraph) if the authors’ continued analysis does end up supporting their initial conclusion that increased competition is associated with a decline<\/b> in the quality of patient data protection, it may show that security has an uphill battle getting into the “service differentiator” list.<\/p>\n

The authors do take a moment to theorize as to why there seems to be an inverse relationship to competition & security:<\/p>\n

We posit that hospitals in more competitive markets may be inclined to shift resources to more consumer visible activities from the less consumer visible activity of data protection<\/i><\/p><\/blockquote>\n

Is That A USB Of Patches In Your Pocket?<\/b><\/p>\n

In “Online Promiscuity: Prophylactic Patching and the Spread of Computer Transmitted Infections<\/a>“, Timothy Kelley & L. Jean Camp examine the efficacy of various aggregate patching and recovery behaviors using real world data and a plethora of interesting simulations.<\/p>\n

If you listened to the SFS “Front Porch”<\/a> conversation with @joshcorman, @armorguy & yours’ truly, you’ll know how I feel about patching, and I believe this paper help support the somewhat progressive approach to both the need for patching but also the need for intelligent patching (with the latter also requiring #spiffy incident response). The authors may say it best, tho:<\/p>\n

We show, using our model and a real world data set, that small increases in patch rates and recovery speed are the most effective approaches to reduce system wide vulnerabilities due to unprotected computers. Our results illustrate that a public health approach may be feasible, as what is required is that a subpopulation adopt prophylactic actions rather than near-universal immunization.<\/i><\/p><\/blockquote>\n

What About The Green Jack?<\/b><\/p>\n

Finally getting to the coding side of the security economics equation, Stephan Neuhaus & Bernhard Plattner look at whether software vulnerability fix rates decrease and if the time between successive fixes goes up as vulnerabilities become fewer and harder to fix in “Software Security Economics: Theory, in Practice<\/a>“.<\/p>\n

They chose Mozilla, Apache httpd and Apache Tomcat as targets of examination and did a thorough investigation of both vulnerability findings and code commits for each product using well-described and documented statistical methods (pretty graphs, too :-).<\/p>\n

Here are the salient bits in their own words:<\/p>\n

Our findings do not support the hypothesis that vulnerability fix rates decline. It seems as if the supply of easily fixable vulnerabilities is not running out and returns are not diminishing (yet).<\/i><\/p><\/blockquote>\n

and:<\/p>\n

With this data and this analysis, we cannot confirm a Red Queen race.<\/i><\/p><\/blockquote>\n

Folks may not be too surprised with the former, but I suspect the latter will also be good conference debate fuel.<\/p>\n

Law & Order : DBU (Data Breach Unit)<\/b><\/p>\n

Sasha Romanosky, David Hoffman & Alessandro Acquisti analyzed court dockets for over 230 federal data breach lawsuits from 2000 to 2010 for their work on “Empirical Analysis of Data Breach Litigation<\/a>“.<\/p>\n

Why look at breach litigation outcomes? For starters, such analysis “can help provide firms with prescriptive guidance regarding the relative chances of being sued, and having to settle<\/i>.” For insurance companies, this type of analysis can also be of help in crafting cyberinsurance policies. It can also help companies that have customer data as their primary asset\/product better understand their obligations as custodians of such information.<\/p>\n

But, you want to know what they found, so here’s the skinny:<\/p>\n

Our results suggest that the odds of a firm being sued are 3.5 times greater<\/b> when individuals suffer financial harm, but 6 times lower when the firm provides free credit monitoring. Moreover, defendants settle 30% more often<\/b> when plaintiffs allege financial loss, or when faced with a certified class action suit. By providing the first comprehensive empirical analysis of data breach litigation, these findings offer insights in the debate over privacy litigation versus privacy regulation.<\/i><\/p><\/blockquote>\n

It’s a quick read and should be something you forward to your legal & compliance folk.<\/p>\n

Achievement: Unlocked<\/b><\/p>\n

On a topic close to home, Toshihiko Takemura & Ayako Komatsu investigate “Who Sometimes Violates the Rule of the Organizations?: Empirical Study on Information Security Behaviors and Awareness<\/a>“.<\/p>\n

The authors develop a behavioral model based on:<\/p>\n