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 …
Machine learning-enabled iot security: Open issues and challenges under advanced persistent threats
Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to
vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely …
vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely …
Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques
The high proportion of energy consumed in buildings has engendered the manifestation of
many environmental problems which deploy adverse impacts on the existence of mankind …
many environmental problems which deploy adverse impacts on the existence of mankind …
Intrusion detection in internet of things systems: a review on design approaches leveraging multi-access edge computing, machine learning, and datasets
The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic
increase of network data and placed a high computation complexity across various …
increase of network data and placed a high computation complexity across various …
IoT intrusion detection taxonomy, reference architecture, and analyses
This paper surveys the deep learning (DL) approaches for intrusion-detection systems
(IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps …
(IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps …
Modeling energy-efficient building loads using machine-learning algorithms for the design phase
FE Sapnken, MM Hamed, B Soldo, JG Tamba - Energy and Buildings, 2023 - Elsevier
Very little work has been done on the feasibility of Machine Learning (ML) for predicting
buildings energy demand right at the design stage. This feasibility, if proven, would help to …
buildings energy demand right at the design stage. This feasibility, if proven, would help to …
[HTML][HTML] Machine learning approach of detecting anomalies and forecasting time-series of IoT devices
With the development of smart cities infrastructure, these cities' energy efficiency has
become a major problem. Many public buildings, including health centers, educational …
become a major problem. Many public buildings, including health centers, educational …
[PDF][PDF] Anomaly Detection for Internet of Things Cyberattacks.
M Alanazi, A Aljuhani - Computers, Materials & Continua, 2022 - researchgate.net
The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of
improving quality of service and facilitating human lives. The IoT revolution has redefined …
improving quality of service and facilitating human lives. The IoT revolution has redefined …
An Ensemble‐Based Multiclass Classifier for Intrusion Detection Using Internet of Things
Internet of Things (IoT) is the fastest growing technology that has applications in various
domains such as healthcare, transportation. It interconnects trillions of smart devices through …
domains such as healthcare, transportation. It interconnects trillions of smart devices through …
[PDF][PDF] A hybrid deep learning-based intrusion detection system for IoT networks
NW Khan, MS Alshehri, MA Khan, S Almakdi… - Math. Biosci …, 2023 - aimspress.com
The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential
applications, but the security of IoT networks remains a major concern. The existing system …
applications, but the security of IoT networks remains a major concern. The existing system …