495 citations · 1.1k across the 10 of their papers we have counts for
9 papers · 1 filter
Poisoning Web-Scale Training Datasets is Practical
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo +6
Deep learning models are often trained on distributed, web-scale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionall…
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra +3
Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learn…
Automatic Yara Rule Generation Using Biclustering
Edward Raff, Richard Zak, Gary Lopez Munoz +5
Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-in…
KiloGrams: Very Large N-Grams for Malware Classification
Edward Raff, William Fleming, Richard Zak +4
N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of are tested, with $n…
Detecting Homoglyph Attacks with a Siamese Neural Network
Jonathan Woodbridge, Hyrum S. Anderson, Anjum Ahuja +1
A homoglyph (name spoofing) attack is a common technique used by adversaries to obfuscate file and domain names. This technique creates process or domain names that are visually si…
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
Hyrum S. Anderson, Phil Roth
This paper describes EMBER: a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes…
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…
Predicting Domain Generation Algorithms with Long Short-Term Memory Networks
Jonathan Woodbridge, Hyrum S. Anderson, Anjum Ahuja +1
Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In or…
DeepDGA: Adversarially-Tuned Domain Generation and Detection
Hyrum S. Anderson, Jonathan Woodbridge, Bobby Filar
Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domain…