A Review on Intrusion Detection in IoT Networks: Existing Challenges

This study introduces a novel approach to enhance the efficiency and reliability of suspicious activity alert systems in network security. Our proposed solution utilizes an ensemble learning-based approach, which leverages multiple deep learning techniques. By combining the strengths of these models, we aim to enhance stratification correctness and minimize the fall-outs. Our system is specifically designed to identify suspicious activities by analyzing network traffic data, such as source IP address, destination IP address, port number, and protocol type. By detecting patterns that deviate from normal behavior, such as unusual connections or excessive data transfer, the system can trigger alerts that notify network administrators of potential security threats. The effectiveness of our proposed approach is demonstrated through rigorous experimental testing using real-world network traffic data. Additionally, our system can adapt and learn over time, making it more effective in detecting and preventing security breaches. In summary, our research contributes to the development of highly efficient and reliable suspicious activity alert systems in network security. Furthermore, our findings shed light on the potential benefits of ensemble learning techniques in this domain.