<p>Though IoT has provided previously unfathomable levels of connectedness and convenience, it has also left IoT devices vulnerable to a broad spectrum of security concerns. Cyberattacks are many and complicated these days, thus traditional methods of protecting IoT are inadequate. This paper presents a hybrid IoT Security Model meant to enable precise and quick threat categorization in IoT settings by combining sophisticated deep learning technologies as LSTM, BERT, RoBERTa, and Transfer Learning. To efficiently capture the semantic and temporal components of assault patterns, the model blends LSTM’s sequential learning capacity with BERT’s and RoBERTa’s contextual knowledge. Models that have been pre-trained are fine-tuned using transfer learning on security datasets pertinent to IoT, therefore reducing processing expenses and enhancing model adaptability. By ensuring a thorough examination of both structured and unstructured data streams, the hybrid method assures accurate categorization of many attack types including distributed DoS, malware injections, and advanced persistent threats. Using benchmark IoT security datasets, the suggested architecture is assessed and compared to current state-of-the-art models. The results indicate that the model is strong and efficient as it has improved accuracy requirements. The paper also emphasizes how the method may be scaled and used in real-time in growing IoT networks. Our efforts to provide robust and adaptable solutions to protect future IoT networks by means of important IoT security issues.</p>