Enhancing Smart Home Security through Machine Learning and Natural Language Processing in IoT

This paper investigates the enhancement of smart home security through the integration of machine learning (ML)algorithms and natural language processing (NLP) techniques within the Internet of Things (IoT). The objective is todevelop a robust framework capable of automating the analysis of safety occurrence reports, thereby improving theextraction of relevant information and identifying underlying causes of security incidents. Employing advanced deeplearning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)networks, alongside topic modeling approaches like Latent Dirichlet Allocation (LDA), the study demonstrates asubstantial reduction in security incidents within real-world smart home environments. Results indicate that theLSTM model achieved a classification accuracy of ۹۲%, while predictive analytics successfully anticipated ۸۵% ofsecurity incidents during a three-month pilot implementation. This leads to a significant ۲۵% decline in reportedsecurity issues. The findings underscore the critical role of integrating ML and NLP techniques in fosteringproactive safety management, ultimately advocating for their broader adoption to enhance smart home security.frameworks and adapt continuously to evolving threats in modern domestic settings

کلیدواژه ها:

Smart home security ، Machine learning ، Natural language processing ، Internet of Things ، Safety management ، Convolutional Neural Networks ، Recurrent Neural Networks ، Topic modeling