[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 …

Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review

MA Alsoufi, S Razak, MM Siraj, I Nafea, FA Ghaleb… - Applied sciences, 2021 - mdpi.com
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 …

Graph neural networks for anomaly detection in industrial Internet of Things

Y Wu, HN Dai, H Tang - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
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 …

A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment

H Aydın, Z Orman, MA Aydın - Computers & Security, 2022 - Elsevier
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 …

A comprehensive study of anomaly detection schemes in IoT networks using machine learning algorithms

A Diro, N Chilamkurti, VD Nguyen, W Heyne - Sensors, 2021 - mdpi.com
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 …

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 …

Intrusion detection and prevention in fog based IoT environments: A systematic literature review

CA de Souza, CB Westphall, RB Machado, L Loffi… - Computer Networks, 2022 - Elsevier
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 …

[HTML][HTML] Deep neural networks in the cloud: Review, applications, challenges and research directions

KY Chan, B Abu-Salih, R Qaddoura, AZ Ala'M… - Neurocomputing, 2023 - Elsevier
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 …

Artificial intelligence techniques for enhancing supply chain resilience: A systematic literature review, holistic framework, and future research

A Kassa, D Kitaw, U Stache, B Beshah… - Computers & Industrial …, 2023 - Elsevier
Today's supply chains (SC) have become vulnerable to unexpected and ever-intensifying
disruptions from myriad sources. Consequently, the concept of supply chain resilience …

NE-GConv: A lightweight node edge graph convolutional network for intrusion detection

T Altaf, X Wang, W Ni, RP Liu, R Braun - Computers & Security, 2023 - Elsevier
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 …