Tejas Kannan (University of Chicago), Synthia Qia Wang (University of Chicago), Max Sunog (University of Chicago), Abraham Bueno de Mesquita (University of Chicago Laboratory Schools), Nick Feamster (University of Chicago), Henry Hoffmann (University of Chicago)

Smart Televisions (TVs) are internet-connected TVs that support video streaming applications and web browsers. Users enter information into Smart TVs through on-screen virtual keyboards. These keyboards require users to navigate between keys with directional commands from a remote controller. Given the extensive functionality of Smart TVs, users type sensitive information (e.g., passwords) into these devices, making keystroke privacy necessary. This work develops and demonstrates a new side-channel attack that exposes keystrokes from the audio of two popular Smart TVs: Apple and Samsung. This side-channel attack exploits how Smart TVs make different sounds when selecting a key, moving the cursor, and deleting a character. These properties allow an attacker to extract the number of cursor movements between selections from the TV's audio. Our attack uses this extracted information to identify the likeliest typed strings. Against realistic users, the attack finds up to 33.33% of credit card details and 60.19% of common passwords within 100 guesses. This vulnerability has been acknowledged by Samsung and highlights how Smart TVs must better protect sensitive data.

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GraphGuard: Detecting and Counteracting Training Data Misuse in Graph...

Bang Wu (CSIRO's Data61/Monash University), He Zhang (Monash University), Xiangwen Yang (Monash University), Shuo Wang (CSIRO's Data61/Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Shirui Pan (Griffith University), Xingliang Yuan (Monash University)

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The Impact of Workload on Phishing Susceptibility: An Experiment

Sijie Zhuo (University of Auckland), Robert Biddle (University of Auckland and Carleton University, Ottawa), Lucas Betts, Nalin Asanka Gamagedara Arachchilage, Yun Sing Koh, Danielle Lottridge, Giovanni Russello (University of Auckland)

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REPLICAWATCHER: Training-less Anomaly Detection in Containerized Microservices

Asbat El Khairi (University of Twente), Marco Caselli (Siemens AG), Andreas Peter (University of Oldenburg), Andrea Continella (University of Twente)

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