Design & analysis of Deep Learning based Defense Mechanisms against Cyber Attacks in IoT - Harish Motekar, Amit Patl, Vinod Rathod, Rahul R. Papalkar, Abrar S. Alvi, Rajkumar Sawant (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research focuses on the crucial challenge of protecting Internet of Things (IoT) networks from various cyber threats, specifically zero-day attacks. Presenting a defense solution based on Convolutional Neural Networks (CNN), specifically designed to accommodate the unique operational constraints and resource limitations of the IoT. Creating a model that can effectively detect threats and is efficient for deployment on IoT devices with limited resources is the major objective. This project is based on a thorough examination of the BoT IoT dataset, which includes a wide range of simulated IoT network behaviors, covering regular operations and suspicious activities. This dataset forms the foundation for developing and evaluating our model. The CNN model we have developed features a simplified architecture that aims to minimize resource usage and enable real-time data analysis. By conducting thorough data preprocessing and rigorous training, the model is fine-tuned to accurately differentiate between harmless and harmful network patterns. After conducting the evaluation phase with the BoT IoT dataset, impressive results were obtained. The model achieved an accuracy rate of 95.98% and an F1 score of 0.8707. The metrics demonstrate the model’s exceptional ability to accurately detect potential security threats in IoT networks. One interesting aspect of our approach involves incorporating a dynamic blacklisting mechanism that is tailored to improve the model’s effectiveness in identifying zero-day attacks. Through regular updates to the blacklist using up-to-date threat intelligence and sending immediate alerts to administrators, the system enables quick responses and mitigation tactics against new threats, enhancing the security of IoT environments. This research validates the effectiveness of utilizing the proposed hybrid CNN model in IoT environments, especially with edge computing approaches, to enhance decentralized and effective threat detection in deployment scenarios. Moreover, the model includes continuous learning capabilities, enabling constant adaptation to emerging cyber threats. With a focus on ethical and security considerations, including protection against adversarial attacks and data privacy, the model’s adaptability is strengthened, making it more reliable in IoT security frameworks. The research project concludes by introducing a CNN-based security mechanism that is both flexible and known for accurate detection; it also has the ability to detect zero-day threats through dynamic blacklisting. In order to safeguard IoT networks from the ever-evolving cyber threat landscape, this comprehensive method is a huge leap forward.