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20172022
most citedAttack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models

18 citations · 23 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CR2022★ 2 cited

Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks

Jurijs Nazarovs, Jack W. Stokes, Melissa Turcotte +2

Ransomware attacks are increasing at an alarming rate, leading to large financial losses, unrecoverable encrypted data, data leakage, and privacy concerns. The prompt detection of…

cs.CR2021

URLTran: Improving Phishing URL Detection Using Transformers

Pranav Maneriker, Jack W. Stokes, Edir Garcia Lazo +3

Browsers often include security features to detect phishing web pages. In the past, some browsers evaluated an unknown URL for inclusion in a list of known phishing pages. However,…

cs.CR2021

Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance

Jack W. Stokes, Paul England, Kevin Kane

Recent research has successfully demonstrated new types of data poisoning attacks. To address this problem, some researchers have proposed both offline and online data poisoning de…

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.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.CR2017★ 18 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…