Research Output
A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things
  The Internet of Things (IoT) is the connection of smart devices and objects to the internet, allowing them to share and analyze data, communicate with each other, and be controlled remotely. Several IoT devices are designed to collect, process, and store confidential data in order to perform their intended function. This information can be sensitive such as location, health, military, financial information, and biometric data. The efficient implementation of IoT networks has become increasingly reliant on security. In IoT networks, several researchers used intrusion detection systems (IDS) for the identification of cyberattacks where machine learning (ML) and deep learning (DL) are significant components. The existing IDS still needs improvements for the detection of multiclass detection to identify each category of attack separately. To improve the detection performance of IDS, this study proposes a hybrid scheme of convolutional neural networks (CNN) and gated recurrent units (GRU). The proposed hybrid scheme integrates two CNN layers and three GRU layers. The proposed scheme was assessed using the IoTID20 dataset.


Momand, A., Jan, S. U., & Ramzan, N. (2024). A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things. In Intelligent Systems and Pattern Recognition: Third International Conference, ISPR 2023, Hammamet, Tunisia, May 11–13, 2023, Revised Selected Papers, Part II (277-287).



Convolutional Neural Networks, Gated Recurrent Units, Internet of Things, Intrusion Detection

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