Adversarial Malware

Adversarial malware research focuses on creating malicious software designed to evade detection by machine learning-based antivirus systems. Current research emphasizes developing sophisticated attack methods, often employing reinforcement learning, evolutionary algorithms, and generative adversarial networks (GANs) to generate functionally-preserved malware that bypasses detectors. This work is crucial for improving the robustness of malware detection systems and understanding the limitations of current machine learning approaches in cybersecurity, ultimately impacting the development of more resilient defense mechanisms.

Papers

August 5, 2024

On the Robustness of Malware Detectors to Adversarial Samples
Muhammad Salman, Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Muhammad Ikram, Sidharth Kaushik, Mohamed Ali Kaafar
Adversarial Attack Native Robustness Adversarial Example Adversarial Sample Adversarial Malware Sensitive Hashing

May 24, 2024

TrojanForge: Generating Adversarial Hardware Trojan Examples with Reinforcement Learning
Amin Sarihi, Peter Jamieson, Ahmad Patooghy, Abdel-Hameed A. Badawy
Reinforcement Learning Generative Adversarial Network Adversarial Example Attack Success Rate Hardware Trojan Adversarial Malware

May 20, 2024

EGAN: Evolutional GAN for Ransomware Evasion
Daniel Commey, Benjamin Appiah, Bill K. Frimpong, Isaac Osei, Ebenezer N. A. Hammond, Garth V. Crosby
Adversarial Malware Attack Framework Ransomware Attack Modeling Technique Semantic Evolution GAN

February 29, 2024

How to Train your Antivirus: RL-based Hardening through the Problem-Space
Ilias Tsingenopoulos, Jacopo Cortellazzi, Branislav Bošanský, Simone Aonzo, Davy Preuveneers, Wouter Joosen, Fabio Pierazzi, Lorenzo Cavallaro
Adversarial Example Adversarial Training Adversarial Malware Adversarial Capability Based Malware Detection

February 23, 2024

A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing
Daniel Gibert, Giulio Zizzo, Quan Le, Jordi Planes
Adversarial Attack Adversarial Text Robust Defense Smoothing Factor Smoothed Classifier Adversarial Malware Based Malware

February 4, 2024

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
Brian Etter, James Lee Hu, Mohammedreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
Malware Detection Obfuscation Technique Deep Reinforcement Learning Approach Adversarial Malware Based Malware Proactive Cyber Defense

October 5, 2023

Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations
Ahmed Abusnaina, Yizhen Wang, Sunpreet Arora, Ke Wang, Mihai Christodorescu, David Mohaisen
Malware Detection Adversarial Malware Bit Flip Attack

August 31, 2023

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
Maria Rigaki, Sebastian Garcia
Adversarial Attack Real Power Model Based Reinforcement Learning Malware Detection Malware Attack Adversarial Malware

August 19, 2023

A Comparison of Adversarial Learning Techniques for Malware Detection
Pavla Louthánová, Matouš Kozák, Martin Jureček, Mark Stamp
Adversarial Attack Adversarial Learning Consistent Comparison Malware Detection Malware Attack Adversarial Method Adversarial Malware

August 17, 2023

Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
Daniel Gibert, Giulio Zizzo, Quan Le
Adversarial Attack Malware Detection Randomized Smoothing Adversarial Malware Based Malware Deep Detector

July 11, 2023

ATWM: Defense against adversarial malware based on adversarial training
Kun Li, Fan Zhang, Wei Guo
Adversarial Example Adversarial Training Adversarial DEfense Adversarial Malware

June 23, 2023

Creating Valid Adversarial Examples of Malware
Matouš Kozák, Martin Jureček, Mark Stamp, Fabio Di Troia
Adversarial Attack Adversarial Example Malware Attack Malware Analysis Adversarial Malware

May 22, 2023

FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign
Kun Li, Fan Zhang, Wei Guo
Adversarial Attack Adversarial Malware

April 14, 2023

Combining Generators of Adversarial Malware Examples to Increase Evasion Rate
Matouš Kozák, Martin Jureček
Adversarial Attack Malware Detection Malware Attack Malware Analysis Adversarial Malware Multi Layer Generator

February 22, 2023

PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
Deqiang Li, Shicheng Cui, Yun Li, Jia Xu, Fu Xiao, Shouhuai Xu
Adversarial Training Evasion Attack Adversarial Malware Based Malware Detection

July 12, 2022

Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Luca Demetrio, Battista Biggio, Fabio Roli
Machine Learning Adversarial Example Case Study Adversarial Malware Security Evaluation

April 16, 2022

SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
Marjan Golmaryami, Rahim Taheri, Zahra Pooranian, Mohammad Shojafar, Pei Xiao
Internet of Thing Malware Detection Internet of Thing Environment Adversarial Malware

April 13, 2022

Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions
Maria Rigaki, Sebastian Garcia
Surrogate Model Malware Detection Malware Attack Adversarial Malware Malware Classifier False Negative Rate

December 3, 2021

Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach
James Lee Hu, Mohammadreza Ebrahimi, Hsinchun Chen
Causal Language Black Box Adversarial Attack Adversarial Malware

November 22, 2021

A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries
Prithviraj Dasgupta, Zachariah Osman
Consistent Comparison State of the Art Adversarial Malware Malware Classifier Based Malware

Adversarial Malware

Papers

On the Robustness of Malware Detectors to Adversarial Samples

TrojanForge: Generating Adversarial Hardware Trojan Examples with Reinforcement Learning

EGAN: Evolutional GAN for Ransomware Evasion

How to Train your Antivirus: RL-based Hardening through the Problem-Space

A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

A Comparison of Adversarial Learning Techniques for Malware Detection

Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing

ATWM: Defense against adversarial malware based on adversarial training

Creating Valid Adversarial Examples of Malware

FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign

Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks

Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware

SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment

Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions

Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach

A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries