Deep learning for cybersecurity in smart grids: Review and perspectives

J Ruan, G Liang, J Zhao, H Zhao, J Qiu… - Energy Conversion …, 2023 - Wiley Online Library
Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered
significant attention in recent years. The application of artificial intelligence (AI), particularly …

Efficient and Secure Data Storage for Future Networks: Review and Future Opportunities

AS Alsalim, MA Javed - IEEE Access, 2024 - ieeexplore.ieee.org
With the increase in the number of Internet of Things (IoT) applications, the reliance on
robust networking, reliable communications, and efficient and secure data storage is …

Physically unclonable functions (PUFs): A new frontier in supply chain product and asset tracking

J Davies, Y Wang - IEEE Engineering Management Review, 2021 - ieeexplore.ieee.org
In this article, we introduce and explore the early implications of an innovative technological
development known as physically unclonable functions (PUFs). We review the main …

Use of machine learning for Web Denial-of-service attacks: a multivocal literature review

M Ayub, O Lajam, A Alnajim, M Niazi - Arabian Journal for Science and …, 2023 - Springer
Abstract Denial-of-service (DoS) attacks conducted on online systems cause the targeted
resources to become inoperative. This is caused by the abnormal traffic intentionally …

Algoritmos de seguridad para mitigar riesgos de datos en la nube: un mapeo sistemático

CJ Guaigua Bucheli - 2021 - dspace.ups.edu.ec
La nube es un concepto muy poderoso que está bajo ataque por el volumen de datos
almacenados y servicios importantes para los consumidores. Tiene muchas ventajas para …

Attack-resilient fault detection for interconnected systems under DoS attack

Q Liu, Y Long, T Li, CLP Chen - ISA transactions, 2024 - Elsevier
In light of the expanding cyber-space applications, the imperative consideration of cyber-
attack ramifications on system security is evident. This paper presents a resilient dynamic …

An ensemble deep learning-based cyber attack detection system using optimization strategy

V Rasikha, P Marikkannu - Knowledge-Based Systems, 2024 - Elsevier
Background of the study Cyber attack stories become a routine in which new levels of
intention are shown by the cyber attacker through sophisticated attacks on networks. The …

Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack

P Kumar, C Kushwaha, D Sethi, D Ghosh, P Gupta… - PloS one, 2025 - journals.plos.org
In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have
become a prevalent form of cybercrime. These attacks are relatively easy to execute but can …

Variational autoencoder for IoT botnet detection

OK CU, D Pranavi, BRA Laxmi… - … Intelligence for the Dark …, 2022 - igi-global.com
IoT devices are naturally vulnerable to security issues such as botnet attacks that lead to
compromised data. Due to the proliferation of network traffic, the existing system …

[PDF][PDF] Using autoencoder feature residuals to improve network intrusion detection

B Lewandowski - 2023 - digital.wpi.edu
Network intrusion detection is a constantly evolving field with researchers and practitioners
constantly working to keep up with novel attacks and growing amounts of network data …