Enhancing IOT Intrusion Detection System Security Using Decision Tree Algorithm
Keywords:
Anomaly Detection, IoT Security, Adaptive Machine Learning, Threat Detection, Comparative AnalysisAbstract
This study presents a comparative analysis of various anomaly detection techniques aimed at enhancing security in Internet of Things (IoT) environments. As IoT devices continue to proliferate, ensuring robust security measures becomes increasingly critical. By employing adaptive machine learning approaches, the study evaluates the effectiveness of different methods in identifying and mitigating IoT-specific security threats. The techniques examined include statistical approaches, clustering algorithms, and deep learning models. Through detailed experimentation and assessment, the research explores each method’s performance in terms of accuracy, scalability, and efficiency in detecting abnormal activities within IoT networks. The findings emphasize the crucial role of adaptive machine learning in addressing the dynamic nature of IoT security challenges and provide recommendations for developing efficient anomaly detection systems tailored for IoT ecosystems.
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