Review: Build a Roadmap for Stepping Into the Field of Anti-Malware Research Smoothly

Han, Wanjiang; Xue, Jingfeng; Wang, Yong; Zhu, Shibing; Kong, Zixiao

doi:10.1109/access.2019.2945787

Cited by 9 publications

(1 citation statement)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the number of adversarial attacks will continue to increase in the future, as economic interests evolve. Only by continuously strengthening the protective barrier of the deep learning model can a secure network security environment be built [12] (…”

Section: Why Study Adversarial Attackmentioning

confidence: 99%

A Survey on Adversarial Attack in the Age of Artificial Intelligence

Kong

Xue

Wang

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the “Why? → What? → How?” research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews.

show abstract

Section: Why Study Adversarial Attackmentioning

confidence: 99%