4 citations · 5 across the 5 of their papers we have counts for
5 papers
CloudLens: Modeling and Detecting Cloud Security Vulnerabilities
Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar
Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration. With their growing popularity, concerns about security vulne…
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…
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…