The Internet of Things (IoT) has transformed cyber-physical systems (CPS) by enabling smart automation, remote monitoring, and real-time decision-making across industries including home automation, industrial control, and smart infrastructure [1]. However, this link raises significant privacy and security concerns that compromise the reliability and safety of these systems [2]. This study provides a comprehensive review of the current research on security concerns, attack detection, authentication methods, and the role of computational intelligence in protecting IoT-enabled CPS [4]. Emphasising both heritage and new attack surfaces, we examine multilayered security concerns across industrial CPS, smart homes, and home automation systems. Advanced authentication methods—multi-factor authentication and One-Time Passwords (OTP)—as well as machine learning (ML), deep neural networks (DNN), and evolutionary computing (EC) for threat detection are also examined. The report also highlights research gaps and offers next actions towards robust, smart, and scalable security systems [7].