Microsoft Malware Classification Challenge

Abstract

The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 research papers. Here we provide a high-level comparison of the publications citing the dataset. The comparison simplifies finding potential research directions in this field and future performance evaluation of the dataset.

References in corpus (10)

• Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
• Imbalanced Malware Images Classification: a CNN based Approach
• Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
• DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
• Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features
• Lempel-Ziv Jaccard Distance, an Effective Alternative to Ssdeep and Sdhash
• Random Forest for Malware Classification
• On the Feasibility of Malware Authorship Attribution
• A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
• ITect: Scalable Information Theoretic Similarity for Malware Detection

Cited by in corpus (30)

• Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
• Deep learning at the shallow end: Malware classification for non-domain experts
• EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
• Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
• Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classification
• Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
• Machine Learning in Cyber-Security - Problems, Challenges and Data Sets
• On the Feasibility of Malware Authorship Attribution
• Generation & Evaluation of Adversarial Examples for Malware Obfuscation
• Malware Detection with LSTM using Opcode Language
• DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining
• Deep Transfer Learning for Static Malware Classification
• Enhancing the Insertion of NOP Instructions to Obfuscate Malware via Deep Reinforcement Learning
• COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
• tf-Darshan: Understanding Fine-grained I/O Performance in Machine Learning Workloads
• A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels
• Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems
• Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
• ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples
• Learning from Context: Exploiting and Interpreting File Path Information for Better Malware Detection
• Throttling Malware Families in 2D
• Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions
• Malware Classification Using Transfer Learning
• Hacking Neural Networks: A Short Introduction
• Dynamic data fusion using multi-input models for malware classification
• AI assisted Malware Analysis: A Course for Next Generation Cybersecurity Workforce
• Malfustection: Obfuscated Malware Detection and Malware Classification with Data Shortage by Combining Semi-Supervised and Contrastive Learning
• Sequential Embedding-based Attentive (SEA) classifier for malware classification
• A Novel Methodology For Crowdsourcing AI Models in an Enterprise
• MMF: A loss extension for feature learning in open set recognition