Adversarial attacks against iot networks using conditional gan based learning
H Benaddi, M Jouhari, K Ibrahimi… - … 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
During the last decade, the integration of artificial intelligence (AI) and the use of intrusion
detection systems (IDSs) in the Internet of Things (IoT) networks have brought a new …
detection systems (IDSs) in the Internet of Things (IoT) networks have brought a new …
MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical Systems with Multi-Process Generative Adversarial Networks*
Y Zhou, J Wang, J Tang, C Gou, Z Jiang… - … of Things and …, 2023 - ieeexplore.ieee.org
Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking
to control a hybrid physical system consisting of different functional subsystems, making the …
to control a hybrid physical system consisting of different functional subsystems, making the …
A framework for anomaly detection in IoT networks using conditional generative adversarial networks
I Ullah, QH Mahmoud - IEEE Access, 2021 - ieeexplore.ieee.org
While anomaly detection and the related concept of intrusion detection are widely studied,
detecting anomalies in new operating behavior in environments such as the Internet of …
detecting anomalies in new operating behavior in environments such as the Internet of …
[HTML][HTML] Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning
As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices
remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the …
remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the …
Enhancing Cybersecurity: The Development of a Flexible Deep Learning Model for Enhanced Anomaly Detection
H Gonaygunta, GS Nadella, PP Pawar… - 2024 Systems and …, 2024 - ieeexplore.ieee.org
Using expert systems and relevant machine learning methods, automating network intrusion
detection has become commonplace. However, the interconnectedness of many industrial …
detection has become commonplace. However, the interconnectedness of many industrial …
Deep feature selection for anomaly detection based on pretrained network and gaussian discriminative analysis
Deep learning neural network serves as a powerful tool for visual anomaly detection (AD)
and fault diagnosis, attributed to its strong abstractive interpretation ability in the …
and fault diagnosis, attributed to its strong abstractive interpretation ability in the …
Anomaly detection based on selection and weighting in latent space
With the high requirements of automation in the era of Industry 4.0, anomaly detection plays
an increasingly important role in high safety and reliability in the production and …
an increasingly important role in high safety and reliability in the production and …
A new explainable deep learning framework for cyber threat discovery in industrial IoT networks
Industrial Internet of Things (IIoT) and Industry 4.0 empower interrelation among
manufacturing processes, industrial machines, and utility services. The time-critical data …
manufacturing processes, industrial machines, and utility services. The time-critical data …
Mitigating Insider Threat: A Neural Network Approach for Enhanced Security
Detecting insider threats is the foremost challenge in many institutions because of the
abnormal behavior of legitimate access and network crawling in the Internet of Things (IoT) …
abnormal behavior of legitimate access and network crawling in the Internet of Things (IoT) …
An area-efficient implementation of recurrent neural network core for unsupervised anomaly detection
T Sakuma, H Matsutani - 2020 IEEE Symposium in Low-Power …, 2020 - ieeexplore.ieee.org
Toward on-device anomaly detection for time-series data, in this paper, we analyze Echo
State Network (ESN), which is a simple form of Recurrent Neural Networks (RNNs), and …
State Network (ESN), which is a simple form of Recurrent Neural Networks (RNNs), and …