Enhancing IoT Network Security Through Deep Learning-Based Intrusion Detection
DOI:
https://doi.org/10.31185/wjcms.363Keywords:
machine learning techniques, deep neural networks, IoT security, LLT attack detection, intrusion detection systemsAbstract
The internet of today is a mainstream aspect of life, with increasingly more devices in the Internet of Things (IoT) ecosystem becoming networked. Although they are leaders in the market today, IoT networks are beset by increasingly more degrading security problems that confuse neatly defined solutions. These weaknesses need to be halted because new threats continue to challenge the resilience of IoT networks. Although the existing methodologies are made to provide more security, there remains untapped potential in machine learning development. Certain of the machine learning and deep learning methods, and benchmark data sets, used for IoT security enhancement are described in this paper. It proposes a new deep learning-based Legitimate Load Testing (LLT) attack detection algorithm implemented in Python and supported by libraries such as TensorFlow, scikit-learn, and Seaborn. Experimental outcomes confirm that the deep learning model improves attack detection precision significantly, which results in more effective countermeasures for protecting IoT networks.
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