Abstract
Although there exist various strategies for IoT Intrusion Detection, this research article sheds light on the aspect of how the application of top 10 Artificial Intelligence - Deep Learning Models can be useful for both supervised and unsupervised learning related to the IoT network traffic data. It pictures the detailed comparative analysis for IoT Anomaly Detection on sensible IoT gadgets that are instrumental in detecting IoT anomalies by the usage of the latest dataset IoT-23. Many strategies are being developed for securing the IoT networks, but still, development can be mandated. IoT security can be improved by the usage of various deep learning methods. This exploration has examined the top 10 deep-learning techniques, as the realistic IoT-23 dataset for improving the security execution of IoT network traffic. We built up various neural network models for identifying 5 kinds of IoT attack classes such as Mirai, Denial of Service (DoS), Scan, Man in the Middle attack (MITM-ARP), and Normal records. These attacks can be detected by using a "softmax" function of multiclass classification in deep-learning neural network models. This research was implemented in the Anaconda3 environment with different packages such as Pandas, NumPy, Scipy, Scikit-learn, TensorFlow 2.2, Matplotlib, and Seaborn. The utilization of AI-deep learning models embraced various domains like healthcare, banking and finance, findings and scientific researches, and the business organizations along with the concepts like the Internet of Things. We found that the top 10 deep-learning models are capable of increasing the accuracy; minimize the loss functions and the execution time for building that specific model. It contributes a major significance to IoT anomaly detection by using emerging technologies Artificial Intelligence and Deep Learning Neural Networks. Hence the alleviation of assaults that happen on an IoT organization will be effective. Among the top 10 neural networks, Convolutional neural networks, Multilayer perceptron, and Generative Adversarial Networks (GANs) output the highest accuracy scores of 0.996317, 0.996157, and 0.995829 with minimized loss function and less time pertain to the execution. This article added to completely grasp the quirks of irregularity identification of IoT anomalies. Henceforth, this research analysis depicts the implementations of the Top 10 AI-deep learning models, which come in handy that assist you to perceive different neural network models and IoT anomaly detection better.
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