Exploring Artificial Intelligence and Data Science-Based Security and its Scope in IoT Use Cases

The fast growth of IO networks has resulted in a security crisis besides the development of decentralized-based innovations, and such decentralized bases or technologies also made challenges in terms of speed, performance, and scalability. Traditional machine learning-based intrusion detection systems (IDS) are unable to manage the intricate and non-linear correlations seen in massive amounts of IoT data. They produce relatively low detection rates, especially in multi-class classification, where many attack types must be addressed. Overcoming these hurdles calls for frameworks: innovative enough to accommodate the challenge whilst using the wealth of data produced by IoT devices. Abstract In this paper, we introduce a unique MLP-based deep learning architecture for intrusion detection in IoT settings. This framework includes a preprocessing pipeline that optimally normalizes and applies one-hot-encoding to the data to prepare it optimally for classification. We tested the algorithms on the UNSW-NB15 dataset, commonly used for IDS. Mere quantitative results show that MLP surpasses classical models like Logistic Regression, SVM, and Random Forests, giving a precision of 97.53%, recall of 97.23%, and accuracy of 97.73% on the multi-class classification task. This framework is undoubtedly scalable and provides a sufficient security mechanism for the whole IoT ecosystem; hence, it can be used in various actual use cases. This performance shows that it could solve the new threats developing in IoT environments.