Enhancing Cybersecurity in IoT Networks: A Deep Learning-Based Intrusion Detection System
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Abstract
The proliferation of Internet of Things (IoT) devices has led to increased security threats, necessitating robust intrusion detection mechanisms. This paper introduces a deep learning-based Intrusion Detection System (IDS) tailored for IoT environments. The proposed model integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to detect cyber threats, including Denial-of-Service (DoS) attacks, malware infections, and unauthorized access. Experimental results on benchmark IoT security datasets demonstrate high accuracy, low false-positive rates, and real-time threat detection capabilities. The study discusses deployment challenges and provides insights into improving IoT network security through AI-driven methods.
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References
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