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A Survey on Adversarial Attacks for Malware Analysis by Aryal, Kshitiz; Gupta, Maanak; Abdelsalam, Mahmoud is a scholarly article available to read on EtoBox.

What is A Survey on Adversarial Attacks for Malware Analysis about?

Machine learning has witnessed tremendous growth in its adoption and advancement in the last decade. The evolution of machine learning from traditional algorithms to modern deep learning architectures has shaped the way today's technology functions. Its unprecedented ability to discover knowledge/patterns from unstructured data and automate the decision-making process led to its application in wide domains. High flying machine learning arena has been recently pegged back by the introduction of adversarial attacks. Adversaries are able to modify data, maximizing the classification error of the models. The discovery of blind spots in machine learning models has been exploited by adversarial attackers by generating subtle intentional perturbations in test samples. Increasing dependency on data has paved the blueprint for ever-high incentives to camouflage machine learning models. To cope with probable catastrophic consequences in the future, continuous research is required to find vulnerabilities in form of adversarial and design remedies in systems. This survey aims at providing the encyclopedic introduction to adversarial attacks that are carried out against malware detection system

Author

Aryal, Kshitiz; Gupta, Maanak; Abdelsalam, Mahmoud

Published

2021

Language

EN