Seth Hastings, Tyler Moore, Corey Bolger, Philip Schumway (University of Tulsa)

This paper presents a method for reduction and aggregation of raw authentication logs into user-experience focused "event logs". The event logs exclude non-interactive authentication data and capture critical aspects of the authentication experience to deliver a distilled representation of an authentication. This method is demonstrated using real data from a university, spanning three full semesters. Event construction is presented along with several examples to demonstrate the utility of event logs in the context of a Security Operations Center (SOC). Authentication success rates are shown to widely vary, with the bottom 5% of users failing more than one third of authentication events. A proactive SOC could utilize such data to assist struggling users. Event logs can also identify persistently locked out users. 2.5% of the population under study was locked out in a given week, indicating that interventions by SOC analysts to reinstate locked-out users could be manageable. A final application of event logs can identify problematic applications with above average authentication failure rates that spike periodically. It also identifies lapsed applications with no successful authentications, which account for over 50% of unique applications in our sample.

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Sticky Fingers: Resilience of Satellite Fingerprinting against Jamming Attacks

Joshua Smailes (University of Oxford), Edd Salkield (University of Oxford), Sebastian Köhler (University of Oxford), Simon Birnbach (University of Oxford), Martin Strohmeier (Cyber-Defence Campus, armasuisse S+T), Ivan Martinovic (University of Oxford)

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Not your Type! Detecting Storage Collision Vulnerabilities in Ethereum...

Nicola Ruaro (University of California, Santa Barbara), Fabio Gritti (University of California, Santa Barbara), Robert McLaughlin (University of California, Santa Barbara), Ilya Grishchenko (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara)

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GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity

Qiushi Wu (University of Minnesota), Zhongshu Gu (IBM Research), Hani Jamjoom (IBM Research), Kangjie Lu (University of Minnesota)

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