6 citations · 16 across the 11 of their papers we have counts for
7 papers · 1 filter
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies
Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar
Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs significant (monetary) loss for a commercial orga…
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…
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…
EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses
Mikhail Kazdagli, Ling Huang, Vijay Reddi +1
Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs agains…