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 …
Deep reinforcement learning for anomaly detection: A systematic review
Anomaly detection has been used to detect and analyze anomalous elements from data for
years. Various techniques have been developed to detect anomalies. However, the most …
years. Various techniques have been developed to detect anomalies. However, the most …
Cyber threats detection in smart environments using SDN-enabled DNN-LSTM hybrid framework
Internet of Things (IoT) is an instantly exacerbated communication technology that is
manifesting miraculous effectuation to revolutionize conventional means of network …
manifesting miraculous effectuation to revolutionize conventional means of network …
Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing
Abstract The Internet of Things (IoT) is an important virtual network that allows remote users
to access linked multimedia devices. The development of IoT and its ubiquitous application …
to access linked multimedia devices. The development of IoT and its ubiquitous application …
A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models
Abstract The Internet of Things (IoT) is extensively used in modern-day life, such as in smart
homes, intelligent transportation, etc. However, the present security measures cannot fully …
homes, intelligent transportation, etc. However, the present security measures cannot fully …
Crsf: An intrusion detection framework for industrial internet of things based on pretrained cnn2d-rnn and svm
S Li, G Chai, Y Wang, G Zhou, Z Li, D Yu, R Gao - IEEE Access, 2023 - ieeexplore.ieee.org
The traditional support vector machine (SVM) requires manual feature extraction to improve
classification performance and relies on the expressive power of manually extracted …
classification performance and relies on the expressive power of manually extracted …
[HTML][HTML] Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection …
Abstract Intrusion Detection Systems (IDS) have become pivotal in safeguarding information
systems against evolving threats. Concurrently, Concept Drift presents a significant …
systems against evolving threats. Concurrently, Concept Drift presents a significant …
A distributed SDN-based intrusion detection system for IoT using optimized forests
K Luo - Plos one, 2023 - journals.plos.org
Along with the expansion of Internet of Things (IoT), the importance of security and intrusion
detection in this network also increases, and the need for new and architecture-specific …
detection in this network also increases, and the need for new and architecture-specific …
[HTML][HTML] Security analysis of cyber physical system using digital forensic incident response
There is a great demand for an efficient security tool which can secure IIoT systems from
potential adversarial attacks. However, it is challenging to design a suitable security model …
potential adversarial attacks. However, it is challenging to design a suitable security model …
A blockchain-based intrusion detection system using viterbi algorithm and indirect trust for iiot systems
The industrial internet of things (IIoT) is considered a new paradigm in the era of wireless
communication for performing automatic communication in the network. However, automatic …
communication for performing automatic communication in the network. However, automatic …