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20182021
most citedSmartphone Impostor Detection with Built-in Sensors and Deep Learning

3 citations · 5 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CR2021

CloudShield: Real-time Anomaly Detection in the Cloud

Zecheng He, Ruby B. Lee

In cloud computing, it is desirable if suspicious activities can be detected by automatic anomaly detection systems. Although anomaly detection has been investigated in the past, i…

cs.CR20211 cited

Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support

Guangyuan Hu, Zecheng He, Ruby B. Lee

Impostors are attackers who take over a smartphone and gain access to the legitimate user's confidential and private information. This paper proposes a defense-in-depth mechanism t…

cs.CR2020

New Models for Understanding and Reasoning about Speculative Execution Attacks

Zecheng He, Guangyuan Hu, Ruby Lee

Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through cov…

cs.CR20203 cited

Smartphone Impostor Detection with Built-in Sensors and Deep Learning

Guangyuan Hu, Zecheng He, Ruby Lee

In this paper, we show that sensor-based impostor detection with deep learning can achieve excellent impostor detection accuracy at lower hardware cost compared to past work on sen…

cs.CR2018

Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning

Zecheng He, Aswin Raghavan, Guangyuan Hu +2

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid syste…