[HTML][HTML] IoT anomaly detection methods and applications: A survey
A Chatterjee, BS Ahmed - Internet of Things, 2022 - Elsevier
Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly
expanding field. This growth necessitates an examination of application trends and current …
expanding field. This growth necessitates an examination of application trends and current …
Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review
The Internet of Things (IoT) concept has emerged to improve people's lives by providing a
wide range of smart and connected devices and applications in several domains, such as …
wide range of smart and connected devices and applications in several domains, such as …
Graph neural networks for anomaly detection in industrial Internet of Things
The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment
The fact that cloud systems are under the increasing risks of cyber attacks has made the
phenomenon of information security first a need and then a necessity for these systems …
phenomenon of information security first a need and then a necessity for these systems …
A comprehensive study of anomaly detection schemes in IoT networks using machine learning algorithms
The Internet of Things (IoT) consists of a massive number of smart devices capable of data
collection, storage, processing, and communication. The adoption of the IoT has brought …
collection, storage, processing, and communication. The adoption of the IoT has brought …
Graph neural networks for intrusion detection: A survey
T Bilot, N El Madhoun, K Al Agha, A Zouaoui - IEEE Access, 2023 - ieeexplore.ieee.org
Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
Intrusion detection and prevention in fog based IoT environments: A systematic literature review
Abstract Currently, the Internet of Things is spreading in all areas that apply computing
resources. An important ally of the IoT is fog computing. It extends cloud computing and …
resources. An important ally of the IoT is fog computing. It extends cloud computing and …
[HTML][HTML] Deep neural networks in the cloud: Review, applications, challenges and research directions
Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …
in a wide range of important real-world applications. DNNs consist of a huge number of …
NE-GConv: A lightweight node edge graph convolutional network for intrusion detection
Resource constraint devices are now the first choice of cyber criminals for launching
cyberattacks. Network Intrusion Detection Systems (NIDS) play a critical role in the detection …
cyberattacks. Network Intrusion Detection Systems (NIDS) play a critical role in the detection …
Survey of graph neural networks and applications
The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …
and video classification). In these applications, deep learning models are trained by …