aspg-21-3186-1791475949 20261008161229 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2025 25 2 Thangam .S Department of Computer Science and Engineering, Amrita School of Computing, Bengaluru, Amrita Vishwa Vidyapeetham, India Jana .S Department of Electronics & Communication Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, India Neutrosophic Logic is an offspring study region in which every intention is projected to hold the proportion of indeterminacy in a subset I, the percentage of truth in a subset T, and the percentage of falsity in subset F. Neutrosophic set (NS) has been effectively used for indeterminate data processing, and establishes benefits to handle with the indeterminacy information of data and is quite a method stimulated for classification application and data analysis. NS delivers an effective and precise method to describe imbalanced data as per the features of the data. Recently, the usage of the Internet of Things (IoT) has enlarged rapidly, and cyber security effects have enlarged beside it. On the state-of-the-art of cyber security is Artificial Intelligence (AI), which employed for the progress of intricate techniques to defense systems and networks, containing IoT systems. Though, cyber-attackers have determined how to develop AI and have started to utilize adversarial AI for accomplishing cybersecurity threats. Therefore, this study designs a new Interval-Valued Neutrosophic Set using Optimization Algorithm-Based Intrusion Detection System (IVNSOA-IDS) technique in IoT cybersecurity. The key objective of the IVNSOA-IDS method rests in the automatic identification of intrusion detection in IoT cybersecurity. In the IVNSOA-IDS technique, data pre-processing is executed to convert the raw data into a compatible format. Besides, the interval-valued neutrosophic set (IVNS) model has been utilized for the automated identification of intrusion detection. Finally, an improved whale optimization algorithm (IWOA) is employed for the better hyperparameter tuning of the IVNS classifier. To demonstrate the enhanced performance of the IVNSOA-IDS technique, an extensive of simulations take place and the performances are inspected under distinct aspects. The experimental outcome reported the advancement of the IVNSOA-IDS methodology under various metrics. 2025 268 278 3186 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.250223 https://www.americaspg.com/journal/21/article/3186 https://www.americaspg.com/storage/61737217721.pdf