Advances in Machine Learning and Reinforcement Learning for IoT Security and Adaptive Systems
Keywords:
Machine Learning, Reinforcement Learning, IoT Security, Adaptive SystemsAbstract
The convergence of machine learning (ML), deep learning (DL), and reinforcement learning (RL) has significantly transformed the landscape of intelligent systems, with applications ranging from robotics and Internet of Things (IoT) security to adaptive data processing pipelines. This study provides a comprehensive exploration of both supervised and unsupervised learning paradigms, examining their theoretical underpinnings, algorithmic frameworks, and practical deployments across diverse domains. Supervised learning techniques, particularly in IoT device authentication and anomaly detection, demonstrate high accuracy and reliability but often demand substantial labeled datasets (Kadhim, 2019). In contrast, unsupervised approaches facilitate clustering and pattern recognition in dynamic and unstructured environments, proving valuable in networking applications and social spam detection (Yau et al., 2019; Rao et al., 2021). Reinforcement learning, with its trial-and-error methodology and delayed reward mechanisms, has shown exceptional adaptability in robotics, adaptive ETL pipelines, and aviation systems (Singh et al., 2022; Vuppala, 2025; Razzaghi et al., 2022). Deep learning models, particularly convolutional neural networks, are examined in the context of adversarial robustness, gait-based biometric analysis, and real-valued function approximation (Su et al., 2019; Sakata et al., 2019; Gullapalli, 1990). The paper also investigates the integration of ML and RL in IoT security frameworks, highlighting lightweight authentication, attacker behavior modeling, and physical access control mechanisms (Chatterjee et al., 2019; Sun et al., 2019; Punithavathi et al., 2019; Singla & Sharma, 2019). Limitations including data scarcity, delayed reinforcement effects, and computational overheads are critically analyzed, alongside emerging solutions such as adaptive self-learning pipelines and multi-stage deep architectures. The study concludes with an extensive discussion on future research directions, emphasizing explainable AI, scalable IoT security models, and the continued evolution of reinforcement-based adaptive systems.
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