Lianying Zhao (Concordia University), Mohammad Mannan (Concordia University)

Unauthorized data alteration has been a long-standing threat since the emergence of malware. System and application software can be reinstalled and hardware can be replaced, but user data is priceless in many cases. Especially in recent years, ransomware has become high-impact due to its direct monetization model. State-of-the-art defenses are mostly based on known signature or behavior analysis, and more importantly, require an uncompromised OS kernel. However, malware with the highest software privileges has shown its obvious existence.

We propose to move from current detection/recovery based mechanisms to data loss prevention, where the focus is on armoring data instead of counteracting malware. Our solution,
Inuksuk, relies on today’s Trusted Execution Environments (TEEs), as available both on the CPU and storage device, to achieve programmable write protection. We back up a copy of user-selected files as write-protected at all times, and subsequent updates are written as new versions securely through TEE. We implement Inuksuk on Windows 7 and 10, and Linux (Ubuntu); our core design is OS and application agnostic, and incurs no run-time performance penalty for applications. File transfer disruption can be eliminated or alleviated through access modes and customizable update policies (e.g., interval, granularity). For Inuksuk’s adoptability in modern OSes, we have also ported Flicker (EuroSys 2008), a defacto standard tool for in-OS privileged TEE management, to the latest 64-bit Windows.

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Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

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Suphannee Sivakorn (Columbia University), Kangkook Jee (NEC Labs America), Yixin Sun (Princeton University), Lauri Korts-Pärn (Cyber Defense Institute), Zhichun Li (NEC Labs America), Cristian Lumezanu (NEC Labs America), Zhenyu Wu (NEC Labs America), Lu-An Tang (NEC Labs America), Ding Li (NEC Labs America)

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Quantity vs. Quality: Evaluating User Interest Profiles Using Ad...

Muhammad Ahmad Bashir (Northeastern University), Umar Farooq (LUMS Pakistan), Maryam Shahid (LUMS Pakistan), Muhammad Fareed Zaffar (LUMS Pakistan), Christo Wilson (Northeastern University)

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Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Shasha Li (University of California Riverside), Ajaya Neupane (University of California Riverside), Sujoy Paul (University of California Riverside), Chengyu Song (University of California Riverside), Srikanth V. Krishnamurthy (University of California Riverside), Amit K. Roy Chowdhury (University of California Riverside), Ananthram Swami (United States Army Research Laboratory)

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