| Full text | |
| Author(s): |
Liu, Jinxin
;
Nogueira, Michele
;
Fernandes, Johan
;
Kantarci, Burak
Total Authors: 4
|
| Document type: | Journal article |
| Source: | IEEE COMMUNICATIONS SURVEYS AND TUTORIALS; v. 24, n. 1, p. 37-pg., 2022-01-01. |
| Abstract | |
Machine Learning (ML) models are susceptible to adversarial samples that appear as normal samples but have some imperceptible noise added to them with the intention of misleading a trained classifier and misclassifying the input. Adversarial Machine Learning (AML) was initially coined following upon researchers pointing out certain blind spots in image classifiers in computer vision field which were exploited by these adversarial samples to deceive the model. Although this has been investigated remarkably in computer vision, the impact of AML in wireless and mobile systems has recently attracted attention. Wireless and mobile networks have intensely benefited from the application of ML classifiers to detect network traffic anomalies and malware detection. However, ML detectors themselves can be exfiltrated/evaded by the samples carefully designed by attackers, raising security concerns for ML-based network applications. Thus, it is crucial to detect such samples to safeguard the network. This survey article presents a systematic mapping and a comprehensive literature review on AML to wireless and mobile systems from physical layer to network and application layers. The article reviews the state-of-the-art AML approaches in the generation and detection of adversarial samples. The samples can be generated by adversarial models such as Generative Adversarial Networks (GANS) and techniques such as Fast Gradient Sign Method (FGSM). The samples can be detected by adversarial models acting as classifiers or ML classifiers reinforced with knowledge on how to detect such samples. For each approach, a high-level overview is provided alongside its impact on solving the problems in wireless and mobile settings. Furthermore, this article provides detailed discussions to highlight the open issues and challenges faced by these approaches, as well as research opportunities which can be of interest to the researchers and developers in Artificial Intelligence (AI)-driven wireless and mobile networking. (AU) | |
| FAPESP's process: | 18/23098-0 - MENTORED: from modeling to experimentation - predicting and detecting DDoS and zero-day attacks |
| Grantee: | Michele Nogueira Lima |
| Support Opportunities: | Research Projects - Thematic Grants |