95 citations · 126 across the 10 of their papers we have counts for
6 papers · 1 filter
Explainable AI for Comparative Analysis of Intrusion Detection Models
Pap M. Corea, Yongxin Liu, Jian Wang +2
Explainable Artificial Intelligence (XAI) has become a widely discussed topic, the related technologies facilitate better understanding of conventional black-box models like Random…
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Wenkai Tan, Justus Renkhoff, Alvaro Velasquez +7
Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions.…
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Justus Renkhoff, Wenkai Tan, Alvaro Velasquez +7
Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure t…
Zero-bias Deep Neural Network for Quickest RF Signal Surveillance
Yongxin Liu, Yingjie Chen, Jian Wang +3
The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open natur…
Class-Incremental Learning for Wireless Device Identification in IoT
Yongxin Liu, Jian Wang, Jianqiang Li +2
Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Non-cr…
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
Yongxin Liu, Jian Wang, Jianqiang Li +4
The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT make it vulnerable to cybersecurity threats. E…