Microsoft Malware Classification Challenge

02/22/2018
by   Royi Ronen, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/13/2015

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Modern malware is designed with mutation characteristics, namely polymor...
research
05/31/2022

Dataset Bias in Android Malware Detection

Researchers have proposed kinds of malware detection methods to solve th...
research
11/29/2021

MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels

Malware family classification is a significant issue with public safety ...
research
08/27/2019

A characterisation of system-wide propagation in the malware landscape

System-wide propagation is frequently observed in malware, and there are...
research
09/13/2019

On educating machines

Machine education is an emerging research field that focuses on the prob...
research
06/27/2023

Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access

We investigate the criminal market dynamics of infostealer malware and p...

Please sign up or login with your details

Forgot password? Click here to reset