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This study proposes a hybrid CNN-LSTM architecture that effectively captures both spatial and temporal patterns in IoT network traffic to achieve approximately 97% accuracy in intrusion detection. By integrating multi-class classification with temporal feature learning, the model addresses the escalating security challenges posed by the rapid proliferation of connected devices. The approach offers a robust framework for identifying diverse attack categories while maintaining stable training performance, making it a relevant advancement for IoT security architectures.
An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks
arXiv:2606.05776v1 Announce Type: cross Abstract: With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification, dataset integration, and temporal feature learning to enhance detection performance in IoT networks. Using network traffic data, the proposed approach is evaluated on intrusion detection tasks and achieves an accuracy of approximately 97%. Experimental results demonstrate that the model effectively detects multiple attack categories while maintaining stable training and validation performance. The integration of convolutional and recurrent neural network components enables the framework to capture both spatial and temporal characteristics of network traffic, improving overall intrusion detection capability in IoT environments.
Abstract:With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification, dataset integration, and temporal feature learning to enhance detection performance in IoT networks. Using network traffic data, the proposed approach is evaluated on intrusion detection tasks and achieves an accuracy of approximately 97%. Experimental results demonstrate that the model effectively detects multiple attack categories while maintaining stable training and validation performance. The integration of convolutional and recurrent neural network components enables the framework to capture both spatial and temporal characteristics of network traffic, improving overall intrusion detection capability in IoT environments.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2606.05776 [cs.CR]
(or arXiv:2606.05776v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.05776 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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