<p>The everyday rapid growth of the Internet of Things(IoT) across many domains like smart home, healthcare, agriculture, and industries has introduced increasingly complex security challenges. This paper presents a comprehensive survey of application-specific IoT security frameworks, highlighting their challenges and strategies, and strengths or limitations. The paper further surveys existing standards, regulations, as well as common attack vectors, including malware, DDoS, firmware tampering, and side channel analysis. In this review, the literature is organized into three stages:(i) IoT security frameworks covering layered architectures, general-purpose, AIML, or application-specific, (ii) security requirements and solutions across major IoT application domains; and (iii) Different IoT security mechanisms spanning hardware, network, and data layers. In addition to surveying and synthesis, this study introduces a novel LLM-based assistant architecture for domain-specific IoT security framework generation. The proposed expert assistant system leverages a fine-tuned open-source LLM or a custom-trained LLM to interpret high-level user intent, including IoT application, IoT device, Hardware, software, special concern objectives, and translates them into transparent, explainable, and actionable security framework suggestions. This review closes the gap between conventional static security models and one-size-fits-all security frameworks and the growing demand for customized, practical, and domain-specific IoT security design, providing both a critical synthesis of current research and a forward-looking AI-assisted framework for intelligent IoT security planning.</p>