Intrusion Detection using IoT with Deep Learning

Author(s)	PRACHI BHIMRAO JADHAV
Country	India
Abstract	The rapid expansion of the Internet of Things (IoT) has led to a surge in interconnected devices, increasing the risk of cyber threats. Traditional Intrusion Detection Systems (IDS) often fail to handle the complexity and dynamic nature of IoT network traffic. This paper introduces a hybrid deep learning model that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN) to enhance intrusion detection performance. Trained on the BoT-IoT dataset, the proposed system achieves high accuracy and reliability, demonstrating its effectiveness in identifying malicious activity in IoT environments.
Keywords	Internet of Things (IoT), Intrusion Detection System (IDS), Cybersecurity in IoT, Deep Learning, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), Hybrid Deep Learning Models, Network Traffic Analysis, Anomaly Detection, Supervised Learning, BoT-IoT Dataset, IoT Network Security, Sequential Data Modeling, Real-Time Threat Detection
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-08-03
DOI	https://doi.org/10.71097/IJSAT.v16.i3.7490
Short DOI	https://doi.org/g9vzgn

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