Adversarial Patterns: Building Robust Android Malware Classifiers

TL;DR

This paper reviews how adversarial machine learning techniques threaten Android malware classifiers, discusses recent attack and defense methods, and offers guidelines for developing more robust models.

Contribution

It provides a comprehensive survey of adversarial attacks and defenses specific to Android malware detection, highlighting future research directions.

Findings

Adversarial attacks can successfully evade Android malware classifiers.

Defense strategies vary in effectiveness against different attack types.

Guidelines for designing robust classifiers are proposed.

Abstract

Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, detect patterns, and make predictions or recommendations. In the field of cybersecurity, these models have made significant improvements in malware detection. However, despite their ability to understand complex patterns from unstructured data, these models are susceptible to adversarial attacks that perform slight modifications in malware samples, leading to misclassification from malignant to benign. Numerous defense approaches have been proposed to either detect such adversarial attacks or improve model robustness. These approaches have resulted in a multitude of attack and defense techniques and the emergence of a field known as `adversarial machine learning.' In this survey paper, we provide a comprehensive review of adversarial machine learning in the context of Android malware classifiers. Android is the most widely used operating system globally and is an easy target for malicious agents. The paper first presents an extensive background on Android malware classifiers, followed by an examination of the latest advancements in adversarial attacks and defenses. Finally, the paper provides guidelines for designing robust malware classifiers and outlines research directions for the future.