Zhuo Cheng (Carnegie Mellon University), Maria Apostolaki (Princeton University), Zaoxing Liu (University of Maryland), Vyas Sekar (Carnegie Mellon University)

Cloud providers deploy telemetry tools in software to perform end-host network analytics. Recent efforts show that sketches, a kind of approximate data structure, are a promising basis for software-based telemetry, as they provide high fidelity for many statistics with a low resource footprint. However, an attacker can compromise sketch-based telemetry results via software vulnerabilities. Consequently, they can nullify the use of telemetry; e.g., avoiding attack detection or inducing accounting discrepancies. In this paper, we formally define the requirements for trustworthy sketch-based telemetry and show that prior work cannot meet those due to the sketch’s probabilistic nature and performance requirements. We present the design and implementation TRUSTSKETCH, a general framework for trustworthy sketch telemetry that can support a wide spectrum of sketching algorithms. We show that TRUSTSKETCH is able to detect a wide range of attacks on sketch-based telemetry in a timely fashion while incurring only minimal overhead.

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Architecting Trigger-Action Platforms for Security, Performance and Functionality

Deepak Sirone Jegan (University of Wisconsin-Madison), Michael Swift (University of Wisconsin-Madison), Earlence Fernandes (University of California San Diego)

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu (Indiana University Bloominton), Di Tang (Indiana University Bloomington), Siyuan Tang (Indiana University Bloomington), Zihao Wang (Indiana University Bloomington), Guanhong Tao (Purdue University), Shiqing Ma (University of Massachusetts Amherst), XiaoFeng Wang (Indiana University Bloomington), Haixu Tang (Indiana University, Bloomington)

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TinyML meets IoBT against Sensor Hacking

Raushan Kumar Singh (IIT Ropar), Sudeepta Mishra (IIT Ropar)

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