most citedLearning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning

182 citations · 243 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CR2018182 cited

Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning

Hyrum S. Anderson, Anant Kharkar, Bobby Filar +2

Machine learning is a popular approach to signatureless malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted…

cs.CR201713 cited

Query-limited Black-box Attacks to Classifiers

Fnu Suya, Yuan Tian, David Evans +1

We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing t…

cs.CR2017

Efficient Dynamic Searchable Encryption with Forward Privacy

Mohammad Etemad, Alptekin Küpçü, Charalampos Papamanthou +1

Searchable symmetric encryption (SSE) enables a client to perform searches over its outsourced encrypted files while preserving privacy of the files and queries. Dynamic schemes, w…

cs.CR2017

Horcrux: A Password Manager for Paranoids

Hannah Li, David Evans

Vulnerabilities in password managers are unremitting because current designs provide large attack surfaces, both at the client and server. We describe and evaluate Horcrux, a passw…

cs.CR20177 cited

Decentralized Certificate Authorities

Bargav Jayaraman, Hannah Li, David Evans

The security of TLS depends on trust in certificate authorities, and that trust stems from their ability to protect and control the use of a private signing key. The signing key is…

cs.CR201741 cited

Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples

Weilin Xu, David Evans, Yanjun Qi

Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier met…