7 citations · 13 across the 5 of their papers we have counts for
6 papers
MalwarePT: A Binary-Level Foundation Model for Malware Analysis
Saastha Vasan, Yuzhou Nie, Kaie Chen +6
Automated malware analysis increasingly relies on machine learning, yet most existing methods remain task-specific and depend on handcrafted features or narrowly scoped models. Rec…
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
Hojjat Aghakhani, Wei Dai, Andre Manoel +7
With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massiv…
VenoMave: Targeted Poisoning Against Speech Recognition
Hojjat Aghakhani, Lea Schönherr, Thorsten Eisenhofer +4
Despite remarkable improvements, automatic speech recognition is susceptible to adversarial perturbations. Compared to standard machine learning architectures, these attacks are si…
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability
Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang +2
A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poison samples are injected into…
Neurlux: Dynamic Malware Analysis Without Feature Engineering
Chani Jindal, Christopher Salls, Hojjat Aghakhani +3
Malware detection plays a vital role in computer security. Modern machine learning approaches have been centered around domain knowledge for extracting malicious features. However,…
Detecting Deceptive Reviews using Generative Adversarial Networks
Hojjat Aghakhani, Aravind Machiry, Shirin Nilizadeh +2
In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic…