Mobile charging stations for electric vehicles—A review
Electric vehicle (EV) penetration is accelerating in an unprecedented way, but the
insufficient charging infrastructure to cover all locations hinders the improvement of the EV …
insufficient charging infrastructure to cover all locations hinders the improvement of the EV …
Comparative research on network intrusion detection methods based on machine learning
C Zhang, D Jia, L Wang, W Wang, F Liu, A Yang - Computers & Security, 2022 - Elsevier
Network intrusion detection system is an essential part of network security research. It
detects intrusion behaviors through active defense technology and takes emergency …
detects intrusion behaviors through active defense technology and takes emergency …
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 …
Light-weight federated learning-based anomaly detection for time-series data in industrial control systems
With the emergence of the Industrial Internet of Things (IIoT), potential threats to smart
manufacturing systems are increasingly becoming challenging, causing severe damage to …
manufacturing systems are increasingly becoming challenging, causing severe damage to …
[HTML][HTML] CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders
Abstract Detecting attacks to Cyber-Physical Systems (CPSs) is of utmost importance, due to
their increasingly frequent use in many critical assets. Intrusion detection in CPSs and other …
their increasingly frequent use in many critical assets. Intrusion detection in CPSs and other …
Zero-day attack detection: a systematic literature review
With the continuous increase in cyberattacks over the past few decades, the quest to
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …
[HTML][HTML] An empirical assessment of ensemble methods and traditional machine learning techniques for web-based attack detection in industry 5.0
Cybersecurity attacks that target software have become profitable and popular targets for
cybercriminals who consciously take advantage of web-based vulnerabilities and execute …
cybercriminals who consciously take advantage of web-based vulnerabilities and execute …
Anomaly detection in 6G networks using machine learning methods
While the cloudification of networks with a micro-services-oriented design is a well-known
feature of 5G, the 6G era of networks is closely related to intelligent network orchestration …
feature of 5G, the 6G era of networks is closely related to intelligent network orchestration …
An ensemble framework with improved hybrid breeding optimization-based feature selection for intrusion detection
Intrusion detection is a proactive means to detect network attacks and has been a hot point
in network security. To address the curse of dimensionality and improve the Intrusion …
in network security. To address the curse of dimensionality and improve the Intrusion …
A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions
Automating the monitoring of industrial processes has the potential to enhance efficiency
and optimize quality by promptly detecting abnormal events and thus facilitating timely …
and optimize quality by promptly detecting abnormal events and thus facilitating timely …