The rapid expansion of Internet of Things (IoT) devices has led to substantial progress in various fields. The diverse and resource-limited characteristics of IoT devices make them susceptible to numerous cyber threats, especially malware. Traditional security approaches fall short of effectively addressing these challenges. In this paper, a novel hybrid approach based on the integration of ensemble learning and fuzzy logic is proposed to enhance IoT security. While the ensemble learning model combines multiple classifiers to improve detection accuracy, fuzzy logic enables a more flexible and interpretable assessment of the security status of IoT systems. Experimental results reveal that the proposed framework provides high-accuracy malware detection and, through the fuzzy system built upon the rule base derived from the ensemble model, offers a more flexible and human intuition-oriented evaluation capability. This study offers an effective solution for ensuring IoT system security, providing an applicable approach across diverse IoT ecosystems.