Enhancing Intrusion Detection Systems in IoT Networks: A Hybrid Approach using CNN, ANN, LSTM, GRU for Improved Security
M. Pradeep, S. Gopalakrishnan · 2024
The automation of daily tasks via the Internet of Things (IoT) has drastically changed people’s lives in the last few years. By connecting different physical devices with various functionalities, this is accomplished. As a result, as IoT networks grow, so does the rate of cyber threats, risking the stability and integrity of data. Numerous intrusion detection systems (IDS s) are currently developed to identify harmful actions based on predefined attacks patterns, thereby safeguarding data against misuse and abnormal attempts. The current IDS have to be improved due to the sharp rise in these types of attacks. The key to enhancing intrusion detection systems is now machining learning. A proposed intrusion detection methodology in this study is hybrid approach. After evaluating the model’s performance using a number of popular, advanced methods currently in use, the suggested model uses CNN, ANN, LSTM and GRU model. The model gives better trade -off compared to other approaches.