56 citations · 138 across the 13 of their papers we have counts for
13 papers
Exploiting Code Symmetries for Learning Program Semantics
Kexin Pei, Weichen Li, Qirui Jin +5
This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We in…
Do you still need a manual smart contract audit?
Isaac David, Liyi Zhou, Kaihua Qin +3
We investigate the feasibility of employing large language models (LLMs) for conducting the security audit of smart contracts, a traditionally time-consuming and costly process. Ou…
Designing a Provenance Analysis for SGX Enclaves
Flavio Toffalini, Mathias Payer, Jianying Zhou +1
Intel SGX enables memory isolation and static integrity verification of code and data stored in user-space memory regions called enclaves. SGX effectively shields the execution of…
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers
Limin Yang, Zhi Chen, Jacopo Cortellazzi +5
Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibili…
Realizable Universal Adversarial Perturbations for Malware
Raphael Labaca-Castro, Luis Muñoz-González, Feargus Pendlebury +3
Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output. Universal Adversarial Perturbations (UAPs), whic…
Identifying Authorship Style in Malicious Binaries: Techniques, Challenges & Datasets
Jason Gray, Daniele Sgandurra, Lorenzo Cavallaro
Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to d…
ROPfuscator: Robust Obfuscation with ROP
Giulio De Pasquale, Fukutomo Nakanishi, Daniele Ferla +1
Software obfuscation plays a crucial role in protecting intellectual property in software from reverse engineering attempts. While some obfuscation techniques originate from the ob…
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
Federico Barbero, Feargus Pendlebury, Fabio Pierazzi +1
Machine learning for malware classification shows encouraging results, but real deployments suffer from performance degradation as malware authors adapt their techniques to evade d…
Catch Me (On Time) If You Can: Understanding the Effectiveness of Twitter URL Blacklists
Simon Bell, Kenny Paterson, Lorenzo Cavallaro
With more than 500 million daily tweets from over 330 million active users, Twitter constantly attracts malicious users aiming to carry out phishing and malware-related attacks aga…
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney +2
Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are com…
BabelView: Evaluating the Impact of Code Injection Attacks in Mobile Webviews
Claudio Rizzo, Lorenzo Cavallaro, Johannes Kinder
A Webview embeds a full-fledged browser in a mobile application and allows the application to expose a custom interface to JavaScript code. This is a popular technique to build so-…
PuppetDroid: A User-Centric UI Exerciser for Automatic Dynamic Analysis of Similar Android Applications
Andrea Gianazza, Federico Maggi, Aristide Fattori +2
Popularity and complexity of malicious mobile applications are rising, making their analysis difficult and labor intensive. Mobile application analysis is indeed inherently differe…
Tracking and Characterizing Botnets Using Automatically Generated Domains
Stefano Schiavoni, Federico Maggi, Lorenzo Cavallaro +1
Modern botnets rely on domain-generation algorithms (DGAs) to build resilient command-and-control infrastructures. Recent works focus on recognizing automatically generated domains…