Based Malware

Based malware detection leverages machine learning, particularly deep learning models, to identify malicious software. Current research heavily focuses on mitigating the challenges posed by concept drift (malware evolution) and adversarial attacks (deliberate evasion techniques), employing methods like generative adversarial networks (GANs), reinforcement learning, and randomized smoothing to improve robustness. These advancements are crucial for enhancing cybersecurity defenses, as effective malware detection is vital for protecting digital infrastructure and mitigating the significant economic and societal impacts of cybercrime.

Papers

May 7, 2024

DREAM: Combating Concept Drift with Explanatory Detection and Adaptation in Malware Classification
Yiling He, Junchi Lei, Zhan Qin, Kui Ren
Concept Drift Textual Explanation ProActive Behavior Malware Datasets Based Malware Malware Representation

April 29, 2024

Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
Daniel Gibert
Deep Learning Machine Learning Classification Code NCD Method Malware Detection Based Malware Current Research

April 14, 2024

Counteracting Concept Drift by Learning with Future Malware Predictions
Branislav Bosansky, Lada Hospodkova, Michal Najman, Maria Rigaki, Elnaz Babayeva, Viliam Lisy
LeArning Abstract Generative Adversarial Network Adversarial Example Adversarial Training Concept Drift Based Malware

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 15, 2023

Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection
Marc Schmitt
Artificial Intelligence Intrusion Detection Network Intrusion Detection Digital Innovation Threat Detection Based Malware Smart Infrastructure BotNet Detection

October 3, 2023

EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis
Dragos Georgian Corlatescu, Alexandru Dinu, Mihaela Gaman, Paul Sumedrea
Malware Analysis Similarity Search Malware Datasets Based Malware

September 25, 2023

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors
Trong-Nghia To, Danh Le Kim, Do Thi Thu Hien, Nghi Hoang Khoa, Hien Do Hoang, Phan The Duy, Van-Hau Pham
Adversarial Attack Diverse Ensemble Adversarial Sample Malware Detection Malware Analysis Based Malware Metamorphic 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

December 12, 2022

Machine Learning for Detecting Malware in PE Files
Collin Connors, Dilip Sarkar
Machine Learning Malware Detection Malware Attack Cyber Threat Based Malware Metamorphic Malware

October 25, 2022

Multi-view Representation Learning from Malware to Defend Against Adversarial Variants
James Lee Hu, Mohammadreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
Malware Attack Multi View Representation Learning Based Malware

February 11, 2022

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers
Limin Yang, Zhi Chen, Jacopo Cortellazzi, Feargus Pendlebury, Kevin Tu, Fabio Pierazzi, Lorenzo Cavallaro, Gang Wang
Backdoor Attack Backdoor Defense State of the Art Defense Jigsaw Puzzle Malware Classifier Based 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

November 8, 2021

OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features
Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Tejaswi Nanjundaswamy, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath
Audio Driven Malware Family Malware Datasets Based Malware Malware Sample Static Feature

Based Malware

Papers

DREAM: Combating Concept Drift with Explanatory Detection and Adaptation in Malware Classification

Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research

Counteracting Concept Drift by Learning with Future Malware Predictions

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

Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection

EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors

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

Machine Learning for Detecting Malware in PE Files

Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers

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

OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features