Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT

Abstract

The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification.

Exploring a Hybrid Technique to Detect Network Intrusions in IoT Systems using PCC, ADASYN, and ALMA

“…The methods used are DesnseNet and nception time, which yielded an accuracy of 98.4% for multi-class classification with a precision, recall, and F1 score of 99%, 97.9% and 98.5%, respectively. Although the results presented by Tareq et al [2022] show a better performance than our model, the results did not show the performance of each attack category. Secondly, the authors could have reported their system's time and space complexity.…”

“…Two models of intelligent networks were used by Tareq et al [2022] to detect cyber-attacks in IoT-based systems. The methods used are DesnseNet and nception time, which yielded an accuracy of 98.4% for multi-class classification with a precision, recall, and F1 score of 99%, 97.9% and 98.5%, respectively.…”

Network traffic classification based on periodic behavior detection

“…The best-performing classifier of TON IoT published by Tareq et al [55] is based on a 2D convolutional network (CNN) with very long packet-length data (SPLT with all packets from connection) organized in the image. The SPLT data give the classifier advantage in the opportunity of highquality feature extraction that allows accurate classification.…”

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

“…The fifteen-class classification, characterized by its intricacy and the granular distinction of intrusion types, demonstrated that CNN-LSTM-GRU maintained a high accuracy of 96.90%. This is a commendable achievement, especially when juxtaposed with DeepAK-IoT [37] and Inception Time [34], which represent the upper echelon of performances in this category. Notably, CNN-LSTM-GRU showed marked superiority over LNKDSEA [38] and RNN [39], underscoring the efficacy of the ensemble approach in managing the increased complexity of fine-grained classifications.…”