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.

20papers

Papers

January 23, 2025

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
Ping He, Lorenzo Cavallaro, Shouling Ji
Android Malware Adversarial Malware Malware Detection

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 Adversarial Sample Native Robustness Adversarial Example Sensitive Hashing Adversarial Malware

May 24, 2024

TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning
Amin Sarihi, Peter Jamieson, Ahmad Patooghy, Abdel-Hameed A. Badawy
Reinforcement Learning Generative Adversarial Network Attack Success Rate Hardware Trojan Adversarial Example 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
Ransomware Attack Modeling Technique Adversarial Malware Semantic Evolution GAN Attack Framework

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
Based Malware Detection Adversarial Capability Adversarial Training Adversarial Malware Adversarial Example

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 Malware Smoothing Factor Smoothed Classifier Based Malware Adversarial Text Adversarial Attack Robust Defense

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
Deep Reinforcement Learning Approach Adversarial Malware Based Malware Obfuscation Technique Proactive Cyber Defense Malware Detection

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
Adversarial Malware Bit Flip Attack Malware Detection

August 31, 2023

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

August 19, 2023

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

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 Randomized Smoothing Deep Detector Based Malware Adversarial Malware Malware Detection

July 11, 2023

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

June 23, 2023

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

May 22, 2023

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

April 14, 2023

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

February 22, 2023

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

July 12, 2022

Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Luca Demetrio, Battista Biggio, Fabio Roli
Adversarial Malware Adversarial Example Case Study Machine Learning 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
Malware Detection Adversarial Malware Internet of Thing Internet of Thing Environment

April 13, 2022

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

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 Adversarial Malware Black Box Adversarial Attack

Adversarial Malware

Papers

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems

On the Robustness of Malware Detectors to Adversarial Samples

TrojanForge: Generating Adversarial Hardware Trojan Examples Using 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