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Lecture 6 – Psychology: From Usability and Risk to Scams

Lecture 6 – Psychology: From Usability and Risk to Scams. Security Computer Science Tripos part 2 Ross Anderson. Usability and Psychology. ‘Why Johnny Can’t Encrypt’ – study of encryption program PGP – showed that 90% of users couldn’t get it right give 90 minutes

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Lecture 6 – Psychology: From Usability and Risk to Scams

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  1. Lecture 6 – Psychology:From Usability and Risk to Scams Security Computer Science Tripos part 2 Ross Anderson

  2. Usability and Psychology • ‘Why Johnny Can’t Encrypt’ – study of encryption program PGP – showed that 90% of users couldn’t get it right give 90 minutes • Private / public, encryption / signing keys, plus trust labels was too much – people would delete private keys, or publish them, or whatever • Security is hard – unmotivated users, abstract security policies, lack of feedback … • Much better to have safe defaults (e.g. encrypt and sign everything) • But economics often push the other way …

  3. Usability and Psychology (2) • 1980s concerns with passwords: technical (crack /etc/passwd, LAN sniffer, retry counter) • 1990s concerns: weak defaults, attacks at point of entry (vertical ATM keypads), can the user choose a good password and not write it down? • Our 1998 password trial: control group, versus random passwords, versus passphrase • The compliance problem; and can someone who chooses a bad password harm only himself?

  4. Social Engineering • Use a plausible story, or just bully the target • ‘What’s your PIN so I can cancel your card?’ • NYHA case • Patricia Dunn case • Kevin Mitnick ‘Art of Deception’ • Traditional responses: • mandatory access control • operational security

  5. Social Engineering (2) • Social psychology: • Solomon Asch, 1951: two-thirds of subjects would deny obvious facts to conform to group • Stanley Milgram, 1964: a similar number will administer torture if instructed by an authority figure • Philip Zimbardo, 1971: you don’t need authority: the subjects’ situation / context is enough • The Officer Scott case • And what about users you can’t train (customers)?

  6. Phishing • Started in 2003 with six reported (there had been isolated earlier attacks on AOL passwords) • By 2006, UK banks lost £35m (£33m by one bank) and US banks maybe $200m • Early phish crude and greedy but phishermen learned fast • E.g. ‘Thank you for adding a new email address to your PayPal account’ • The banks make it easy for them – e.g. Halifax

  7. Phishing (2) • Banks pay firms to take down phishing sites • A couple have moved to two-factor authentication (CAP) – we’ll discuss later • At present, the phished banks are those with poor back-end controls and slow asset recovery • One gang (Rockphish) is doing half to two-thirds of the business • Mule recruitment seems to be a serious bottleneck

  8. Types of phishing website • Misleading domain name http://www.banckname.com/ http://www.bankname.xtrasecuresite.com/ • Insecure end user http://www.example.com/~user/www.bankname.com/ • Insecure machine http://www.example.com/bankname/login/ http://49320.0401/bankname/login/ • Free web hosting http://www.bank.com.freespacesitename.com/

  9. Rock-phish is different! • Compromised machines run a proxy • Domains do not infringe trademarks • name servers usually done in similar style • Distinctive URL style http://session9999.bank.com.lof80.info/signon/ • Some usage of “fast-flux” from Feb’07 onwards • viz: resolving to 5 (or 10…) IP addresses at once

  10. Mule recruitment • Proportion of spam devoted to recruitment shows that this is a significant bottleneck • Aegis, Lux Capital, Sydney Car Centre, etc • mixture of real firms and invented ones • some “fast-flux” hosting involved • Only the vigilantes are taking these down • impersonated are clueless and/or unmotivated • Long-lived sites usually indexed by Google

  11. Fake banks • These are not “phishing” • no-one takes them down, apart from the vigilantes • Usual pattern of repeated phrases on each new site, so googling finds more examples • sometimes old links left in (hand-edited!) • Sometimes part of a “419” scheme • inconvenient to show existence of dictator’s $millions in a real bank account! • Or sometimes part of a lottery scam

  12. Fraud and Phishing Patterns • Fraudsters do pretty well everything that normal marketers do • The IT industry has abandoned manuals – people learn by doing, and marketers train them in unsafe behaviour (click on links…) • Banks’ approach is ‘blame and train’ – long known to not work in safety critical systems • Their instructions ‘look for the lock’, ‘click on images not URLs’, ‘parse the URL’ are easily turned round, and discriminate against nongeeks

  13. Results • Ability to detect phishing is correlated with SQ-EQ • It is (independently) correlated with gender • So the gender HCI issue applies to security too

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