EMPOWERING INTRUSION DETECTION IN IOT THROUGH ADVANCED DEEP LEARNING TECHNIQUES

Abstract

The paper provides an extensive discussion of the state-of-the-art artificial intelligence being utilized in deep learning to realize intrusion in IOT-based IoT systems. This paper uses five recent deep learning models (i.e., CNNs, RNNs, AEs, DRL, and Transformers) to compare and contrast network intrusions to identify and classify them in a fashion that is as elegant and delicate as the IoT networks. Several experimental results using representative benchmark IoT have been promising: CNN and Transformer are both 90 percent accurate, but DRL increases its performance in training by 71, 91.2, which suggests that the model learning is adaptable. One of the methods, autoencoders, exhibited the highest validation accuracy (98.33) and therefore demonstrated their unsupervised detection of anomalies. This distinctiveness and significance of the piece is complex comparison with the integration of supervised, unsupervised and reinforcement learning paradigms in the context of resource limited dynamic environs, achieved through IOT-based IoT environments. It becomes the first research to integrate classical architectures with reinforcement learning to react to the recently emerging threats, as well as to the issues peculiar to the IoT world, including the bias in the data, the type of real-time detection, and the resource constraints in devices. A comprehensive performance appraisal, accuracy, recalls, and mean squared error losses were used to ensure the model's robustness/ generalizability of regression and to offer model selection/ optimization processes to match the requirements of operational use. In addition to that the thesis also addresses major problems and limitations related to the practical application of IDS as overlaying of model tuning strategies, distributed learning strategies, federated learning strategies, and the hybrid architectures, which is a tradeoff between the cost of computation and the rate of detection.