CGFuzzer: A fuzzing approach based on coverage-guided generative adversarial networks for industrial IoT protocols
Z Yu, H Wang, D Wang, Z Li… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
With the widespread application of the Industrial Internet of Things (IIoT), industrial control
systems (ICSs) greatly improve industrial productivity, efficiency, and product quality …
systems (ICSs) greatly improve industrial productivity, efficiency, and product quality …
[Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining
X Zhai - Complexity, 2021 - Wiley Online Library
There are complex posture changes in dance movements, which lead to the low accuracy of
dance movement recognition. And none of the current motion recognition uses the dancer's …
dance movement recognition. And none of the current motion recognition uses the dancer's …
Novel machine learning for big data analytics in intelligent support information management systems
Two-dimensional1 arrays of bi-component structures made of cobalt and permalloy elliptical
dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a …
dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a …
Revealing Performance Issues in Server-side WebAssembly Runtimes via Differential Testing
WebAssembly (Wasm) is a bytecode format originally serving as a compilation target for
Web applications. It has recently been used increasingly on the server side, eg, providing a …
Web applications. It has recently been used increasingly on the server side, eg, providing a …
Assessing robustness of image recognition models to changes in the computational environment
Image recognition tasks typically use deep learning and require enormous processing
power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely …
power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely …
Noisecam: Explainable ai for the boundary between noise and adversarial attacks
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 …
domains. However, adversarial attacks can easily mislead a neural network and lead to …
Exploring adversarial attacks on neural networks: An explainable approach
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 …
applications such as autonomous driving. Consequently, it is of great significance to ensure …
Setti: As elf-supervised adv e rsarial malware de t ection archi t ecture in an i ot environment
In recent years, malware detection has become an active research topic in the area of
Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of …
Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of …
DeltaNN: Assessing the impact of computational environment parameters on the performance of image recognition models
Image recognition tasks typically use deep learning and require enormous processing
power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely …
power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely …
D3: Differential Testing of Distributed Deep Learning with Model Generation
Deep Learning (DL) techniques have been widely deployed in many application domains.
The growth of DL models' size and complexity demands distributed training of DL models …
The growth of DL models' size and complexity demands distributed training of DL models …