Second SHB Workshop Liveblogging (5)
David Livingstone Smith moderated the fourth session, about (more or less) methodology.
Angela Sasse, University College London (suggested reading: The Compliance Budget: Managing Security Behaviour in Organisations; Human Vulnerabilities in Security Systems), has been working on usable security for over a dozen years. As part of a project called “Trust Economics,” she looked at whether people comply with security policies and why they either do or do not. She found that there is a limit to the amount of effort people will make to comply—this is less actual cost and more perceived cost. Strict and simple policies will be complied with more than permissive but complex policies. Compliance detection, and reward or punishment, also affect compliance. People justify noncompliance by “frequently made excuses.”
Bashar Nuseibeh, Open University (suggested reading: A Multi-Pronged Empirical Approach to Mobile Privacy Investigation; Security Requirements Engineering: A Framework for Representation and Analysis), talked about mobile phone security; specifically, Facebook privacy on mobile phones. He did something clever in his experiments. Because he wasn’t able to interview people at the moment they did something—he worked with mobile users—he asked them to provide a “memory phrase” that allowed him to effectively conduct detailed interviews at a later time. This worked very well, and resulted in all sorts of information about why people made privacy decisions at that earlier time.
James Pita, University of Southern California (suggested reading: Deployed ARMOR Protection: The Application of a Game Theoretic Model for Security at the Los Angeles International Airport), studies security personnel who have to guard a physical location. In his analysis, there are limited resources—guards, cameras, etc.—and a set of locations that need to be guarded. An example would be the Los Angeles airport, where a finite number of K-9 units need to guard eight terminals. His model uses a Stackelberg game to minimize predictability (otherwise, the adversary will learn it and exploit it) while maximizing security. There are complications—observational uncertainty and bounded rationally on the part of the attackers—which he tried to capture in his model.
Markus Jakobsson, Palo Alto Research Center (suggested reading: Male, late with your credit card payment, and like to speed? You will be phished!; Social Phishing; Love and Authentication; Quantifying the Security of Preference-Based Authentication), pointed out that auto insurers ask people if they smoke in order to get a feeling for whether they engage in high-risk behaviors. In his experiment, he selected 100 people who were the victim of online fraud and 100 people who were not. He then asked them to complete a survey about different physical risks such as mountain climbing and parachute jumping, financial risks such as buying stocks and real estate, and Internet risks such as visiting porn sites and using public wi-fi networks. He found significant correlation between different risks, but I didn’t see an overall pattern emerge. And in the discussion phase, several people had questions about the data. More analysis, and probably more data, is required. To be fair, he was still in the middle of his analysis.
Rachel Greenstadt, Drexel University (suggested reading: Practical Attacks Against Authorship Recognition Techniques (pre-print); Reinterpreting the Disclosure Debate for Web Infections), discussed ways in which humans and machines can collaborate in making security decisions. These decisions are hard for several reasons: because they are context dependent, require specialized knowledge, are dynamic, and require complex risk analysis. And humans and machines are good at different sorts of tasks. Machine-style authentication: This guy I’m standing next to knows Jake’s private key, so he must be Jake. Human-style authentication: This guy I’m standing next to looks like Jake and sounds like Jake, so he must be Jake. The trick is to design systems that get the best of these two authentication styles and not the worst. She described two experiments examining two decisions: should I log into this website (the phishing problem), and should I publish this anonymous essay or will my linguistic style betray me?
Mike Roe, Microsoft, talked about crime in online games, particularly in Second Life and Metaplace. There are four classes of people on online games: explorers, socializers, achievers, and griefers. Griefers try to annoy socializers in social worlds like Second Life, or annoy achievers in competitive worlds like World of Warcraft. Crime is not necessarily economic; criminals trying to steal money is much less of a problem in these games than people just trying to be annoying. In the question session, Dave Clark said that griefers are a constant, but economic fraud grows over time. I responded that the two types of attackers are different people, with different personality profiles. I also pointed out that there is another kind of attacker: achievers who use illegal mechanisms to assist themselves.
In the discussion, Peter Neumann pointed out that safety is an emergent property, and requires security, reliability, and survivability. Others weren’t so sure.
Adam Shostack’s liveblogging is here. Ross Anderson’s liveblogging is in his blog post’s comments. Matt Blaze’s audio is here.
Conference dinner tonight at Legal Seafoods. And four more sessions tomorrow.
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Petréa Mitchell • June 11, 2009 6:13 PM
Hey, it’s the Bartle MUD personality categories! (Except that “griefer” was originally named “killer”.) Here’s the original paper that defined them:
http://www.mud.co.uk/richard/hcds.htm
Which argues that the proportion of killers/griefers can be changed by alterations in the technical and social dynamics of the game world, but it tends to reach a stable state.