A Novel Hybrid Deep Learning Model for Attack Detection in IoT Environment: Convolutional Neural Network with Transformer Approach

Summary

The study presents a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Transformers for detecting attacks in IoT environments. Utilizing the CIC-IoT-2023 dataset, the model achieved high performance metrics: 99.97% accuracy and a loss of 0.0123, demonstrating its effectiveness in real-time attack detection.

Abstract

The rapid development of Internet of Things (IoT) devices has enhanced connectivity and accessibility. Nevertheless, the increasing interconnectivity of these environments presents new threat levels, requiring robust anomaly detection. In this study, a new security method that is based on deep learning is presented to mitigate the particular threats that are associated with Internet of Things networks. The proposed approach employs deep neural networks to efficiently monitor network activity patterns and detect potential vulnerabilities in real-time communication. A hybrid CNN-Transformer model for attack detection and classification in IoT networks is proposed in this study. To create and test this approach, we analyzed the CIC-IoT-2023 dataset, which includes 33 distinct IoT threat types distributed over 7 distinct categories. The experimental findings demonstrate that the proposed model works excellently, with a precision of 99.96%, a recall of 99.96%, an F1 score of 99.96%, and an accuracy of 99.97% with a loss of 0.0123. Moreover, the proposed model is analysed based on computational time and resource consumption, demonstrating its efficacy in detecting and classifying attacks, with minimised computational complexity while maintaining high accuracy.

Introduction

The Internet of Things technology has advanced significantly in recent years, and we have entered a highly interconnected digital world. Many different industries are currently using Internet of Things (IoT) devices, including health care, agricultural activity, transportation, aerospace, and production¹. Renowned experts project that the Internet of Things (IoT) and related applications will significantly affect the world economy by 2025, with annual effects ranging from $3.9 trillion to $11.1 trillion². On the other hand, new security-related difficulties arise due to this flawless connection. As IoT devices have evolv....

Protocol

This section discusses attack detection system-related research materials and methods in the IoT environment. In order to replicate the experiments on the CIC-IoT-2023 dataset, this protocol outlines specific procedures. The subsequent Table of Materials contains a list of all the hardware and software used. Python (pandas, scikit-learn, and TensorFlow Keras) is used to implement the pipeline, which is run on Google Colab with PySpark available for large-file preprocessing. A detailed description of the data preprocessing procedures has been provided.

Dataset
The CIC-IoT-2023 dataset, which was released....

Results

This section presents the evaluation parameters along with the test results.

Performance evaluation metrics
The effectiveness of the models may be evaluated using several defined criteria for evaluation. Accuracy, recall, precision, F1 score, ROC, confusion matrix, and other statistical metrics are shown in Table 4. Insights into the models' overall performance are provided by the metrics.

Table 4: Performance evaluation.......

Discussion

The CNN-Transformer model was tested on the CICIoT2023 datasets, with 80% reserved for training and 20% for testing. The model achieved an accuracy of 99.97%, F1 score of 99.96%, recall of 99.97%, precision of 99.96% and a loss of 0.0123, confirming its effectiveness for IoT intrusion detection. CNN-based approaches, similar to the findings¹³, are proficient in detecting small spatial patterns within network traffic characteristics; however, they often exhibit constraints in modeling long-range de.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Apple M2 (8-core CPU)	Apple	N/A	(OS) provides the environment in which deep learning frameworks and tools operate.
CICIoT2023 Dataset	UNB	https://www.unb.ca/cic/datasets/iot2023.html	Publically available daset of IoT
Keras 2.6.0	Keras	https://keras.io/	An API running on top of TensorFlow, providing a user-friendly interface.
Matplotlib 3.4.3	Matplotlib	https://matplotlib.org/	Visualization library for Python.
NumPy 1.21.4	Numpy	https://numpy.org/	Fundamental package for scientific computing with Python
NVIDIA Tesla P100 GPU (16 GB VRAM)	Google Colab Pro	N/A	Training hardware
Pandas 1.3.4	Pandas	https://pandas.pydata.org/	Data analysis and manipulation tool.
Python 3.8.10	Python Software Foundation	https://www.python.org/downloads/release/python-3810/	The most popular language for deep learning due to its extensive libraries and community support.
scikit-learn 1.0.2	Scikit Learn	https://scikit-learn.org/	Machine learning library for Python.
Seaborn 0.11.2	Seaborn	https://seaborn.pydata.org/	Statistical data visualization library.
System RAM	N/A	N/A	32 GB RAM
TensorFlow 2.6.0	Google	https://www.tensorflow.org/	Widely used for its comprehensive tools and libraries.