Big data analytics deep learning techniques and applications: A survey
HA Selmy, HK Mohamed, W Medhat - Information Systems, 2023 - Elsevier
Deep learning (DL), as one of the most active machine learning research fields, has
achieved great success in numerous scientific and technological disciplines, including …
achieved great success in numerous scientific and technological disciplines, including …
DDoS attacks in Industrial IoT: A survey
S Chaudhary, PK Mishra - Computer Networks, 2023 - Elsevier
As the IoT expands its influence, its effect is becoming macroscopic and pervasive. One of
the most discernible effects is in the industries where it is known as Industrial IoT (IIoT). IIoT …
the most discernible effects is in the industries where it is known as Industrial IoT (IIoT). IIoT …
A novel two-stage deep learning model for network intrusion detection: LSTM-AE
Machine learning and deep learning techniques are widely used to evaluate intrusion
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
[HTML][HTML] Enhancing IoT network security through deep learning-powered Intrusion Detection System
The rapid growth of the Internet of Things (IoT) has brought about a global concern for the
security of interconnected devices and networks. This necessitates the use of efficient …
security of interconnected devices and networks. This necessitates the use of efficient …
Hyperparameter optimization for 1D-CNN-based network intrusion detection using GA and PSO
D Kilichev, W Kim - Mathematics, 2023 - mdpi.com
This study presents a comprehensive exploration of the hyperparameter optimization in one-
dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The …
dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The …
[HTML][HTML] Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things
S Wang, W Xu, Y Liu - Computer Networks, 2023 - Elsevier
Abstract The Internet of Things (IoT), as the information carrier of the Internet and
telecommunications networks, is a new network technology comprising physical entities …
telecommunications networks, is a new network technology comprising physical entities …
Deep Reinforcement Learning for intrusion detection in Internet of Things: Best practices, lessons learnt, and open challenges
Abstract The Internet of Things (IoT) scenario places important challenges even for deep
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
A deep learning integrated blockchain framework for securing industrial IoT
A Aljuhani, P Kumar, R Alanazi… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
The Industrial Internet of Things (IIoT) is a collection of interconnected smart sensors and
actuators with industrial software tools and applications. IIoT aims to enhance manufacturing …
actuators with industrial software tools and applications. IIoT aims to enhance manufacturing …
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 …
SMWE-GFPNNet: A high-precision and robust method for forest fire smoke detection
Smoke is an early manifestation of forest fire. Accurate identification of smoke from forest
fires is crucial for the prevention and control of forest fires, which helps protect the ecological …
fires is crucial for the prevention and control of forest fires, which helps protect the ecological …