Based Malware Detection

Machine learning (ML) is increasingly used for malware detection, aiming to improve accuracy and automation of threat identification. Current research focuses on enhancing the robustness of these ML models against adversarial attacks (evasion techniques) through methods like adversarial training and moving target defenses, as well as improving the explainability of model predictions for better analyst understanding. This work is crucial for bolstering cybersecurity, as effective and explainable malware detection is vital for protecting systems and mitigating the impact of sophisticated attacks.

Papers

September 9, 2024

Explainable Malware Analysis: Concepts, Approaches and Challenges
Harikha Manthena, Shaghayegh Shajarian, Jeffrey Kimmell, Mahmoud Abdelsalam, Sajad Khorsandroo, Maanak Gupta
Technical Challenge Constructive Approach Malware Detection Concept Identification Malware Classification Malware Analysis Based Malware Detection

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

October 24, 2023

Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection
Borja Molina-Coronado, Antonio Ruggia, Usue Mori, Alessio Merlo, Alexander Mendiburu, Jose Miguel-Alonso
Obfuscation Technique Static Analysis Android Malware Based Malware Detection

August 9, 2023

A Feature Set of Small Size for the PDF Malware Detection
Ran Liu, Charles Nicholas
Machine Learning Feature Wise Malware Detection Novel Malware Based Malware Detection

July 27, 2023

Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance
Savino Dambra, Yufei Han, Simone Aonzo, Platon Kotzias, Antonino Vitale, Juan Caballero, Davide Balzarotti, Leyla Bilge
Machine Learning Data Set Feature Extraction Model Performance Malware Detection Malware Classification Malware Family Based Malware Detection

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

February 21, 2023

MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection
Aqib Rashid, Jose Such
Adversarial Attack Adversarial Example Adversarial Threat QuEry Based Attack Provable Defense Based Malware Detection

February 1, 2023

Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection
Aqib Rashid, Jose Such
Adversarial Attack Attack Strategy Adversarial Machine Learning Based Malware Detection Moving Target Defense

June 7, 2022

Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection
Michael D. Wong, Edward Raff, James Holt, Ravi Netravali
Malware Detection Malware Datasets Preserving Transformation Based Malware Detection Malicious Training

February 15, 2022

StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection
Aqib Rashid, Jose Such
Adversarial Attack Adversarial Robustness Adversarial Learning Domain Adversarial Adversarial Threat Based Malware Detection

Based Malware Detection

Papers

Explainable Malware Analysis: Concepts, Approaches and Challenges

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

Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection

A Feature Set of Small Size for the PDF Malware Detection

Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance

PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks

MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection

Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection

Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection

StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection