Visible to the public Biblio

Filters: Author is Harbach, Marian  [Clear All Filters]
2017-08-02
Harbach, Marian, De Luca, Alexander, Egelman, Serge.  2016.  The Anatomy of Smartphone Unlocking: A Field Study of Android Lock Screens. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. :4806–4817.

To prevent unauthorized parties from accessing data stored on their smartphones, users have the option of enabling a "lock screen" that requires a secret code (e.g., PIN, drawing a pattern, or biometric) to gain access to their devices. We present a detailed analysis of the smartphone locking mechanisms currently available to billions of smartphone users worldwide. Through a month-long field study, we logged events from a panel of users with instrumented smartphones (N=134). We are able to show how existing lock screen mechanisms provide users with distinct tradeoffs between usability (unlocking speed vs. unlocking frequency) and security. We find that PIN users take longer to enter their codes, but commit fewer errors than pattern users, who unlock more frequently and are very prone to errors. Overall, PIN and pattern users spent the same amount of time unlocking their devices on average. Additionally, unlock performance seemed unaffected for users enabling the stealth mode for patterns. Based on our results, we identify areas where device locking mechanisms can be improved to result in fewer human errors – increasing usability – while also maintaining security.

2017-04-24
Egelman, Serge, Harbach, Marian, Peer, Eyal.  2016.  Behavior Ever Follows Intention?: A Validation of the Security Behavior Intentions Scale (SeBIS) Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. :5257–5261.

The Security Behavior Intentions Scale (SeBIS) measures the computer security attitudes of end-users. Because intentions are a prerequisite for planned behavior, the scale could therefore be useful for predicting users' computer security behaviors. We performed three experiments to identify correlations between each of SeBIS's four sub-scales and relevant computer security behaviors. We found that testing high on the awareness sub-scale correlated with correctly identifying a phishing website; testing high on the passwords sub-scale correlated with creating passwords that could not be quickly cracked; testing high on the updating sub-scale correlated with applying software updates; and testing high on the securement sub-scale correlated with smartphone lock screen usage (e.g., PINs). Our results indicate that SeBIS predicts certain computer security behaviors and that it is a reliable and valid tool that should be used in future research.

2014-09-17
Fahl, Sascha, Harbach, Marian, Perl, Henning, Koetter, Markus, Smith, Matthew.  2013.  Rethinking SSL Development in an Appified World. Proceedings of the 2013 ACM SIGSAC Conference on Computer &\#38; Communications Security. :49–60.
The Secure Sockets Layer (SSL) is widely used to secure data transfers on the Internet. Previous studies have shown that the state of non-browser SSL code is catastrophic across a large variety of desktop applications and libraries as well as a large selection of Android apps, leaving users vulnerable to Man-in-the-Middle attacks (MITMAs). To determine possible causes of SSL problems on all major appified platforms, we extended the analysis to the walled-garden ecosystem of iOS, analyzed software developer forums and conducted interviews with developers of vulnerable apps. Our results show that the root causes are not simply careless developers, but also limitations and issues of the current SSL development paradigm. Based on our findings, we derive a proposal to rethink the handling of SSL in the appified world and present a set of countermeasures to improve the handling of SSL using Android as a blueprint for other platforms. Our countermeasures prevent developers from willfully or accidentally breaking SSL certificate validation, offer support for extended features such as SSL Pinning and different SSL validation infrastructures, and protect users. We evaluated our solution against 13,500 popular Android apps and conducted developer interviews to judge the acceptance of our approach and found that our solution works well for all investigated apps and developers.