A comprehensive survey on graph anomaly detection with deep learning
Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …
the others in the sample. Over the past few decades, research on anomaly mining has …
Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset
An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
The role of machine learning in network anomaly detection for cybersecurity
A Yaseen - Sage Science Review of Applied Machine …, 2023 - journals.sagescience.org
This research introduces a theoretical framework for network anomaly detection in
cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble …
cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble …
Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects
MY Arafat, MJ Hossain, MM Alam - Renewable and Sustainable Energy …, 2024 - Elsevier
Predictive maintenance is an essential aspect of microgrid operations as it enables
identifying potential equipment failures in advance, reducing downtime, and increasing the …
identifying potential equipment failures in advance, reducing downtime, and increasing the …
Distributed anomaly detection in smart grids: a federated learning-based approach
The smart grid integrates Information and Communication Technologies (ICT) into the
traditional power grid to manage the generation, distribution, and consumption of electrical …
traditional power grid to manage the generation, distribution, and consumption of electrical …
Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence
Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The
current security tools are almost perfect when it comes to identifying and preventing known …
current security tools are almost perfect when it comes to identifying and preventing known …
[HTML][HTML] Proposed algorithm for smart grid DDoS detection based on deep learning
SY Diaba, M Elmusrati - Neural Networks, 2023 - Elsevier
Abstract The Smart Grid's objective is to increase the electric grid's dependability, security,
and efficiency through extensive digital information and control technology deployment. As a …
and efficiency through extensive digital information and control technology deployment. As a …
Ensemble model based on hybrid deep learning for intrusion detection in smart grid networks
U AlHaddad, A Basuhail, M Khemakhem, FE Eassa… - Sensors, 2023 - mdpi.com
The Smart Grid aims to enhance the electric grid's reliability, safety, and efficiency by
utilizing digital information and control technologies. Real-time analysis and state estimation …
utilizing digital information and control technologies. Real-time analysis and state estimation …
Deep SARSA-based reinforcement learning approach for anomaly network intrusion detection system
S Mohamed, R Ejbali - International Journal of Information Security, 2023 - Springer
The growing evolution of cyber-attacks imposes a risk in network services. The search of
new techniques is essential to detect and classify dangerous attacks. In that regard, deep …
new techniques is essential to detect and classify dangerous attacks. In that regard, deep …
[HTML][HTML] An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment
Abstract Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can
be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) …
be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) …