Advanced Abnormality Recognition in IoT Networks Using LSTM-RNNs for Dynamic Security Enhancement

As Internet of Things (IoT) devices proliferate, it is critical to guarantee the security and dependability of IoT networks. Due to the dynamic and diverse nature of IoT networks, traditional anomaly detection approaches often fail to capture these characteristics. This study presents a unique use of long short-term memory recurrent neural networks (LSTMRNNs) for anomaly detection in Internet of Things networks. Because RNNs can naturally record sequential patterns of data, they are especially useful for tracking the temporal relationships that are common in Internet of Things network traffic. Our suggested model architecture makes use of the Long Short-Term Memory (LSTM) version of RNNs, which has been tailored to the particulars of Internet of Things traffic. On an extensive dataset that included a variety of IoT devices and traffic patterns, the suggested model was trained and verified. According to experimental data, our RNN-based strategy achieves higher detection rates with fewer false positives than conventional techniques like the Statistical Thresholding Method and Signature-based Detection. Moreover, the model demonstrates resilience against dynamic threats and flexibility in accommodating the dynamic domain of Internet of Things devices and their corresponding actions. This study opens the door for further research in this area while also highlighting the potential of RNNs to improve IoT network security.