aspg-2-3018-1791417201 20261007235321 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2024 14 2 M. Rajendiran Professor, Department of Computer science and Engineering QIS College of Engineering And Technology, Ongole, Andhra Pradesh .523272, India Jayanthi .E Assistant professor Mohamed sathak A.J College of engineering, India Suganthi .R Associate Professor, Department of ECE, Panimalar Engineering College, Chennai, India M. Jamuna Rani Associate Professor, Department of ECE, M.Kumarasamy College of Engineering (Autonomous) Thalavapalayam, Karur, 639113, India S. Vimalnath Associate Professor, Department of ECE, M.Kumarasamy College of Engineering (Autonomous) Thalavapalayam, Karur, 639113, India In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications. 2024 198 213 3018 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.140214 https://www.americaspg.com/journal/2/article/3018 https://www.americaspg.com/storage/91741209151.pdf