activity
20172020
most citedAttack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models

18 citations · 21 across the 2 of their papers we have counts for

collaborators

6 papers

cs.HC20203 cited

Designing Indicators to Combat Fake Media

Imani N. Sherman, Elissa M. Redmiles, Jack W. Stokes

The growth of misinformation technology necessitates the need to identify fake videos. One approach to preventing the consumption of these fake videos is provenance which allows th…

cs.MM2020

AMP: Authentication of Media via Provenance

Paul England, Henrique S. Malvar, Eric Horvitz +16

Advances in graphics and machine learning have led to the general availability of easy-to-use tools for modifying and synthesizing media. The proliferation of these tools threatens…

cs.CR2019

ScriptNet: Neural Static Analysis for Malicious JavaScript Detection

Jack W. Stokes, Rakshit Agrawal, Geoff McDonald +1

Malicious scripts are an important computer infection threat vector in the wild. For web-scale processing, static analysis offers substantial computing efficiencies. We propose the…

cs.AI2018

Robust Neural Malware Detection Models for Emulation Sequence Learning

Rakshit Agrawal, Jack W. Stokes, Mady Marinescu +1

Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within leg…

cs.CR2018

Neural Classification of Malicious Scripts: A study with JavaScript and VBScript

Jack W. Stokes, Rakshit Agrawal, Geoff McDonald

Malicious scripts are an important computer infection threat vector. Our analysis reveals that the two most prevalent types of malicious scripts include JavaScript and VBScript. Th…

cs.CR201718 cited

Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models

Jack W. Stokes, De Wang, Mady Marinescu +2

Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial at…