Adversarial Patterns: Building Robust Android Malware Classifiers

arXiv:2203.02121v2 Announce Type: replace-cross
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 …

abstract adversarial analyze android arxiv autonomous autonomous vehicles building business classifiers cs.cr cs.lg cybersecurity data detection fields however improvements machine machine learning machine learning models malware malware detection medicine patterns predictions recommendations robust type vast vehicles