Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Abstract

The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda–deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaIoT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature.

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization

“…We compared the DL-BiLSTM model with recent studies ( Shieh et al, 2021 ; Wang et al, 2021 ; Bhardwaj, Mangat & Vig, 2020 ; Qazi, Almorjan & Zia, 2022 ; Qureshi et al, 2021 ; Alharbi et al, 2021 ; Attique, Hao & Ping, 2022 ; Om Kumar et al, 2022 ) on two datasets, CIC IDS2017 and N-BaIoT, in order to further illustrate the superiority of the detection performance of the model provided in this article. The experimental results can be seen in Tables 7 and 8 , and the performance test results not given in the paper are indicated with “-” in the table.…”

Botnet‐based IoT network traffic analysis using deep learning

“…Attique et al 111 address intrusion detection challenges in resource‐constrained smart industries operating on the routing protocol for low power and lossy networks. They introduce a fog‐assisted DL framework called Cu‐DNNGRU, designed to tackle various security threats within these settings.…”

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

“…However, the above models are less effective in detecting rare-class attack data. Methods [57][58][59][60] were tested on the N-BaIoT dataset, and all of them achieved high detection rates, but the above methods did not validate the detection effect on unknown attacks. Comparing the best performance of the proposed KGMS-IDS with the above state-of-the-art intrusion detection methods, KGMS-IDS achieves the highest accuracy rate on both datasets.…”