10th International Congress on Information and Communication Technology

Authors - Franciskus Antonius Alijoyo, N Venkatramana, Omaia AlOmari, Shamim Ahmad Khan, B Kiran Bala
Abstract - The Internet of Things (IoT) is becoming a crucial component of many industries, from smart cities to healthcare, in today's networked world. IoT devices are becoming more and more susceptible to security risks, especially zero-day (0day) attacks, which take advantage of undiscovered flaws. The dynamic and dispersed nature of these systems makes it difficult to identify and mitigate these assaults in IoT contexts. This research focuses on a deep learning model that was created and put into use with Python software. It was made especially to do a detection job with great accuracy. The proposed Autoencoder (AE) with Attention Mechanism model demonstrates exceptional performance in detecting zero-day attacks, achieving an accuracy of 99.45%, precision of 98.56%, recall of 98.53%, and an F1 score of 98.21%. The involvement of the attention mechanism helps to focus on the most relevant features, enhancing its efficiency and reducing computational overhead, making it a promising solution for real-time security applications in IoT systems. Compared to previous methods, such as STL+SVM and AE+DNN, the proposed model significantly outperforms the methods. These results highlight its superior ability to identify anomalies with minimal false positives. Because of its resilience, the model is very good at making zero-attacks. The results demonstrate how deep learning may improve IoT systems' security posture by offering proactive, real-time protections against zero-day threats, resulting in safer and more robust IoT environments.