Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines

K Choi, J Yi, C Park, S Yoon - IEEE access, 2021‏ - ieeexplore.ieee.org
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

Deep learning for time series anomaly detection: A survey

Z Zamanzadeh Darban, GI Webb, S Pan… - ACM Computing …, 2024‏ - dl.acm.org
Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …

Attack graph model for cyber-physical power systems using hybrid deep learning

A Presekal, A Ştefanov, VS Rajkumar… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016.
However, existing attack detection methods are limited. Most of them are based on power …

Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

MA Ferrag, L Maglaras, S Moschoyiannis… - Journal of Information …, 2020‏ - Elsevier
In this paper, we present a survey of deep learning approaches for cyber security intrusion
detection, the datasets used, and a comparative study. Specifically, we provide a review of …

Deep learning for anomaly detection: A survey

R Chalapathy, S Chawla - arxiv preprint arxiv:1901.03407, 2019‏ - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Machine learning driven smart electric power systems: Current trends and new perspectives

MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020‏ - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …

Security of wide-area monitoring, protection, and control (WAMPAC) systems of the smart grid: A survey on challenges and opportunities

S Vahidi, M Ghafouri, M Au, M Kassouf… - … Surveys & Tutorials, 2023‏ - ieeexplore.ieee.org
The evolution of power generation systems, along with their related increase in complexity,
led to the critical necessity of Wide-Area Monitoring, Protection, and Control (WAMPAC) …

Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities

Y Luo, Y **ao, L Cheng, G Peng, D Yao - ACM Computing Surveys …, 2021‏ - dl.acm.org
Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of CPSs and more sophisticated attacks …

DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids

MA Ferrag, L Maglaras - IEEE Transactions on Engineering …, 2019‏ - ieeexplore.ieee.org
In this paper, we propose a novel deep learning and blockchain-based energy framework
for smart grids, entitled DeepCoin. The DeepCoin framework uses two schemes, a …

Detection of real-time malicious intrusions and attacks in IoT empowered cybersecurity infrastructures

IA Kandhro, SM Alanazi, F Ali, A Kehar, K Fatima… - IEEE …, 2023‏ - ieeexplore.ieee.org
Computer viruses, malicious, and other hostile attacks can affect a computer network.
Intrusion detection is a key component of network security as an active defence technology …