aspg-2-2378-1791417298 20261007235458 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2024 13 1 Ahmed Aziz Tashkent State Universtiy of Economics, Tashkent, Uzbekistan Sanjar Mirzaliev Tashkent State Universtiy of Economics, Tashkent, Uzbekistan The ubiquity of interconnected devices within the Internet of Things (IoT) paradigm has revolutionized modern connectivity, simultaneously amplifying the susceptibility of networks to diverse security threats. This study addresses the pressing necessity for robust intrusion detection mechanisms tailored for IoT networks. Utilizing a simulated dataset reflecting a spectrum of network intrusions within a military environment, the research employs sophisticated methodologies, notably harnessing Decision Tree (DT) algorithms optimized via Grey Wolf Optimization (GWO) for hyperparameter tuning. The investigation meticulously evaluates and refines intrusion detection mechanisms, emphasizing the pivotal role of feature importance analysis in fortifying network security. Results demonstrate the efficacy of the optimized DT algorithm in the precise classification of network traffic, illuminating key attributes instrumental for intrusion detection. These findings underscore the significance of adaptive and interpretable detection strategies in mitigating evolving threats within IoT networks, advocating for resilient approaches to bolster network security. 2024 60 68 2378 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.130106 https://www.americaspg.com/journal/2/article/2378 https://www.americaspg.com/storage/61704327783.pdf