Malware Classifier

Malware classifiers are machine learning systems designed to automatically identify malicious software. Current research focuses on improving their accuracy and robustness against adversarial attacks, employing various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, particularly LSTMs), and increasingly, vision transformers (ViTs), often combined with techniques like transfer learning and feature selection. These advancements are crucial for enhancing cybersecurity defenses, as effective malware classification is vital for protecting computer systems and networks from increasingly sophisticated threats.

Papers

September 28, 2024

Accelerating Malware Classification: A Vision Transformer Solution
Shrey Bavishi, Shrey Modi
Malware Detection Malware Classification Novel Architecture Vision Transformer Model Malware Classifier Threat Landscape

August 27, 2024

Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
Hamid Bostani, Zhengyu Zhao, Veelasha Moonsamy
Adversarial Example Adversarial Robustness Global Impact Spurious Correlation Android Malware Malware Classifier

May 4, 2024

CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls
Ahmed Bensaoud, Jugal Kalita
Convolutional Neural Network Transfer Learning Long Short Term Memory Deep Learning Architecture Malware Classification CNN LSTM Malware Classifier

September 30, 2023

Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection
Salim Sazzed, Sharif Ullah
Feature Selection Privacy Policy Malware Classification Enhancing Efficiency Malware Family Malware Classifier

March 20, 2023

DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Shoumik Saha, Wenxiao Wang, Yigitcan Kaya, Soheil Feizi, Tudor Dumitras
Adversarial Learning Malware Detection Randomized Smoothing Certified Robustness Smoothed Classifier Malware Classifier Decentralized Riemannian

November 7, 2022

Interpreting deep learning output for out-of-distribution detection
Damian Matuszewski, Ida-Maria Sintorn
Distribution Detection Neural Network Output Malware Classifier

September 6, 2022

Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection
Sun RuiJin, Guo ShiZe, Guo JinHong, Xing ChangYou, Yang LuMing, Guo Xi, Pan ZhiSong
Deep Neural Network Adversarial Example Malware Detection Malware Classifier Vulnerability Analysis

August 20, 2022

Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations
Dmitrijs Trizna
Hybrid Deep Learning Hybrid Machine Learning Multiple Deep Neural Network Malware Classifier Malicious Sample Malware Representation

August 17, 2022

An Efficient Multi-Step Framework for Malware Packing Identification
Jong-Wouk Kim, Yang-Sae Moon, Mi-Jung Choi
Malware Detection Multi Step Malware Analysis Malware Classifier

August 15, 2022

Self-Supervised Vision Transformers for Malware Detection
Sachith Seneviratne, Ridwan Shariffdeen, Sanka Rasnayaka, Nuran Kasthuriarachchi
Self Supervised Learning Malware Detection Self Supervised Vision Transformer Malware Classifier

August 12, 2022

On deceiving malware classification with section injection
Adeilson Antonio da Silva, Mauricio Pamplona Segundo
Malware Classification Malware Family Malware Classifier Injection Drug Use

May 24, 2022

Fast & Furious: Modelling Malware Detection as Evolving Data Streams
Fabrício Ceschin, Marcus Botacin, Heitor Murilo Gomes, Felipe Pinagé, Luiz S. Oliveira, André Grégio
Concept Drift Malware Detection Malware Classifier Concept Drift Detection Metamorphic Malware

May 13, 2022

Representation learning with function call graph transformations for malware open set recognition
Jingyun Jia, Philip K. Chan
Individual Representation Self Supervised Pre Training Simple Function Open Set Recognition Malware Attack Malware Classification Malware Family Malware Classifier Malware Representation Graph Transformation

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

March 4, 2022

Adversarial Patterns: Building Robust Android Malware Classifiers
Dipkamal Bhusal, Nidhi Rastogi
Adversarial Attack Adversarial Learning Malware Detection Android Malware Adversarial Pattern Malware Classifier

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

December 25, 2021

An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification
Ferhat Demirkıran, Aykut Çayır, Uğur Ünal, Hasan Dağ
Deep Learning Model Diverse Ensemble Pre Trained Transformer Malware Detection Malware Classification Malware Family Malware Classifier Forest Classifier

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

Malware Classifier

Papers

Accelerating Malware Classification: A Vision Transformer Solution

Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations

CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls

Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection

DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness

Interpreting deep learning output for out-of-distribution detection

Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection

Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations

An Efficient Multi-Step Framework for Malware Packing Identification

Self-Supervised Vision Transformers for Malware Detection

On deceiving malware classification with section injection

Fast & Furious: Modelling Malware Detection as Evolving Data Streams

Representation learning with function call graph transformations for malware open set recognition

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

Adversarial Patterns: Building Robust Android Malware Classifiers

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers

An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification

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