6 citations · 10 across the 6 of their papers we have counts for
8 papers
Bandwidth Utilization Side-Channel on ML Inference Accelerators
Sarbartha Banerjee, Shijia Wei, Prakash Ramrakhyani +1
Accelerators used for machine learning (ML) inference provide great performance benefits over CPUs. Securing confidential model in inference against off-chip side-channel attacks i…
Power-Based Attacks on Spatial DNN Accelerators
Ge Li, Mohit Tiwari, Michael Orshansky
With proliferation of DNN-based applications, the confidentiality of DNN model is an important commercial goal. Spatial accelerators, that parallelize matrix/vector operations, are…
Challenges in cybersecurity: Lessons from biological defense systems
Edward Schrom, Ann Kinzig, Stephanie Forrest +18
We explore the commonalities between methods for assuring the security of computer systems (cybersecurity) and the mechanisms that have evolved through natural selection to protect…
SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution
Sarbartha Banerjee, Prakash Ramrakhyani, Shijia Wei +1
Hardware-enclaves that target complex CPU designs compromise both security and performance. Programs have little control over micro-architecture, which leads to side-channel leaks,…
The Shape of Alerts: Detecting Malware Using Distributed Detectors by Robustly Amplifying Transient Correlations
Mikhail Kazdagli, Constantine Caramanis, Sanjay Shakkottai +1
We introduce a new malware detector - Shape-GD - that aggregates per-machine detectors into a robust global detector. Shape-GD is based on two insights: 1. Structural: actions such…
Exploiting Latent Attack Semantics for Intelligent Malware Detection
Mkhail Kazdagli, Constantine Caramanis, Sanjay Shakkottai +1
Behavioral malware detectors promise to expose previously unknown malware and are an important security primitive. However, even the best behavioral detectors suffer from high fals…