Revisiting the Microsoft Malware Classification Challenge (BIG 2015) in 2022

 In 2015, Microsoft provided the data science community with an unprecedented malware dataset and encouraging open-source progress on effective techniques for grouping variants of malware files into their respective families. Formatted as a Kaggle Competition, it featured a very large (for that time) dataset comprising of almost 40GB of compressed files containing disarmed malware samples and their corresponding disassembled ASM code.

On the Intriguing Properties of Backdoored Neural Networks

Introduction 

Malicious actors can alter the expected behavior of a neural network in order to respond to data containing certain triggers only known to the attacker, without disrupting model performance when presented with normal inputs. An adversary will commonly force these misclassifications by either performing trigger injection [19] or dataset poisoning [6]. Less popular techniques that operate at hardware level such as manipulating the binary code of a neural network or the tainting the physical circuitry [26, 8] can be equally effective.