Abstract
Recent years have seen a surge in the usage of deep learning as a critical and adaptable tool for the Intrusion Detection Systems (IDSs) and in the Internet of Things (IoT). Detecting intrusions using deep learning is compared to other techniques, such as machine learning, which have been used in the past, in this research. There is a lot of expertise, advice, and ongoing maintenance required for most solutions, including machine learning. The fundamental advantage of deep learning over other techniques is that it eliminates the majority of the feature extraction process while maintaining high accuracy, efficiency, and system reliability. In this paper, an intrusion detection model is proposed based on deep learning technique. A Multi-Layer Perceptron (MLP) Neural Network model is implemented. The KDDCUP 99’ dataset is used in this research to test two deep learning architectures against each other and with previous works. Four hidden layers of ReLu activation, a softmax activation output, Adam optimizer, and early stopping validation loss monitoring are used in both topologies. Because they are implemented as multi-classification neural networks, they also use categorical cross entropy for loss function calculation. Form (10, 50, 10, 1) were the hidden layers in Model 1, while form (20, 20, 20, 1) were the hidden layers in Model 2. Model 1 architecture achieves a maximum accuracy of 99.88%, while Model 2 architecture achieves a maximum accuracy of 99.785%. The system’s efficiency and accuracy were tested with regard to the size of the samples as a factor.
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Elghamrawy, S.M., Lotfy, M.O., Elawady, Y.H. (2022). An Intrusion Detection Model Based on Deep Learning and Multi-layer Perceptron in the Internet of Things (IoT) Network. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_4
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