  There have been significant issues given the IoT, with heterogeneity of
billions of devices and with a large amount of data. This paper proposed an
innovative design of the Internet of Things (IoT) Environment Intrusion
Detection System (or IDS) using Deep Learning-integrated Convolutional Neural
Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on
the CICIDS2017 dataset, achieved an accuracy of 99.52% in classifying network
traffic as either benign or malicious. The real-time processing capability,
scalability, and low false alarm rate in our model surpass some traditional IDS
approaches and, therefore, prove successful for application in today's IoT
networks. The development and the performance of the model, with possible
applications that may extend to other related fields of adaptive learning
techniques and cross-domain applicability, are discussed. The research
involving deep learning for IoT cybersecurity offers a potent solution for
significantly improving network security.
