Malware Datasets

Malware datasets are crucial for developing and evaluating machine learning models designed to detect and classify malicious software. Current research focuses on improving model accuracy and explainability using various techniques, including deep learning (e.g., convolutional neural networks, hidden Markov models), ensemble methods, and novel feature extraction methods (e.g., from audio, images, or antivirus scan reports). The availability of high-quality, diverse, and realistically challenging datasets, along with the development of robust methods to mitigate adversarial attacks and concept drift, is vital for advancing malware detection capabilities and enhancing cybersecurity.

Papers

July 27, 2024

A Survey of Malware Detection Using Deep Learning
Ahmed Bensaoud, Jugal Kalita, Mahmoud Bensaoud
Deep Learning Timely Survey Malware Detection Malware Datasets

June 4, 2024

kNN Classification of Malware Data Dependency Graph Features
John Musgrave, Anca Ralescu
Classification Code Explanatory Inference Malware Classification Malware Datasets Kaggle Competition Malware Feature

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

December 11, 2023

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
Yuyang Zhou, Guang Cheng, Zongyao Chen, Shui Yu
Android Malware Adversarial Purification Evasion Attack Malware Datasets

November 21, 2023

High-resolution Image-based Malware Classification using Multiple Instance Learning
Tim Peters, Hikmat Farhat
High Resolution Multiple Instance Learning Grayscale Image Malware Classification Multiple Instance Malware Datasets

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 24, 2023

Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset
Suleman Qamar
Novel Dataset Cyber Attack Malware Attack Malware Analysis Malware Datasets

July 17, 2023

Hidden Markov Models with Random Restarts vs Boosting for Malware Detection
Aditya Raghavan, Fabio Di Troia, Mark Stamp
Malware Detection Hidden Markov Model Token Boosting Malware Datasets

July 10, 2023

Impact of Feature Encoding on Malware Classification Explainability
Elyes Manai, Mohamed Mejri, Jaouhar Fattahi
Global Impact High Explainability Explainable Artificial Intelligence Malware Classification XAI Research Feature Encoding Malware Datasets

July 7, 2023

A Natural Language Processing Approach to Malware Classification
Ritik Mehta, Olha Jurečková, Mark Stamp
Deep Learning Natural Language Processing Malware Detection Hidden Markov Model Malware Classification Hidden State Malware Datasets

June 9, 2023

AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora
Robert J. Joyce, Tirth Patel, Charles Nicholas, Edward Raff
Feature Learning Malware Family Malware Datasets Novel Malware

May 31, 2023

Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis
Mst Shapna Akter, Hossain Shahriar, Iysa Iqbal, MD Hossain, M. A. Karim, Victor Clincy, Razvan Voicu
Machine Learning Adversarial Attack Quantum Machine Learning Security Vulnerability Adversarial Scenario Malware Datasets

May 2, 2023

CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario
Jingcai Guo, Song Guo, Shiheng Ma, Yuxia Sun, Yuanyuan Xu
Malware Detection Malware Family Malware Datasets Open Set Scenario Novelty Generation

December 15, 2022

A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection
Saddam Hussain Khan, Wasi Ullah
Ensemble Learning Malware Datasets

November 4, 2022

MalGrid: Visualization Of Binary Features In Large Malware Corpora
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath
Interactive Visualization Malware Attack Malware Datasets Binary Feature Malware Sample

August 13, 2022

On the Limitations of Continual Learning for Malware Classification
Mohammad Saidur Rahman, Scott E. Coull, Matthew Wright
Continual LEArning Continual Learning Fundamental Limitation Malware Classification Malware Datasets

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

November 29, 2021

MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels
Robert J. Joyce, Dev Amlani, Charles Nicholas, Edward Raff
Ground Truth Malware Family Malware Datasets

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