## 3 Integrated Adaptive Security Framework for IoT (IASF-IoT)
### AI-Driven Security Orchestration  
The IASF-IoT framework comprises an AI-driven security engine. This engine acts as an orchestrator that coordinates all the other security components of the framework. This AI engine uses a reinforcement learning model to continuously optimize security measures across the IoT network. The model is represented as  
\[Q(s,a)=Q(s,a)+\alpha[R(s,a)+\gamma\max Q(s^{\prime},a^{\prime})-Q(s,a)], \tag{1}\]  
where \(Q(s,a)\) is the quality of action \(a\) in state \(s\), \(\alpha\) is the learning rate, \(R(s,a)\) is the reward for taking action \(a\) in state \(s\), \(\gamma\) is the discount factor, and \(\max Q(s^{\prime},a^{\prime})\) is the maximum expected future reward. This model allows the system to learn from past security incidents and adapt its strategies in real-time. The primary objective of this reinforcement learning approach in the security context is for the AI agent to learn an optimal, or near-optimal, security policy. This policy maximizes the overall security posture of the IoT network, as quantified by a carefully designed \"security reward function\". This function accumulates from immediate rewards associated with successful threat mitigation, attack prevention, and maintenance of data integrity and privacy. In this context, the state space \(S\) represents system conditions such as current network load, device health status, and detected anomaly indicators. Actions \(A\) include security responses such as throttling traffic, triggering re-authentication, or isolating a node. The reward function \(R(s,a)\) is defined based on the successful mitigation of simulated threats and minimal resource overhead, encouraging actions that maintain both system integrity and efficiency. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Approach** & **Focus Area** & **Key Technologies** & **Strengths** & **Weaknesses** & **References** \\ \hline Adaptive & Scalable and adaptable & Hash-based Signatures & Scalability, adaptable & Complexity in implementation & [46, 47] \\ Authentication & security & & security & implementation & \\ \hline Network Security & Network-level security & Secure Routing & & & \\  & measures & Protocols, Intrusion & & & \\  & measures & Detection Systems & & network protection & \\ \hline Hardware-based & Secure hardware & Trusted Platform & & & \\ Security & elements & Modules (TPMs), & Strong hardware & Cost, complexity of integration & [50, 51] \\ \hline Cross-layer Security & Integrated security & Multi-layer Security & Holistic security & Complexity of implementation & [52, 53] \\  & across IoT layers & Protocols & approach & & \\ \hline Security & IoT security standards & Standard Protocols, & Consistency, & Slow adoption, & \\ Standardization & and protocols & Framewords & interoperability & & regulatory challenges & [54] \\ \hline Context-aware Security & Adaptive security & Contextual Analysis, & Flexible, responsive & & Complexity in & \\  & based on context & Adaptive Algorithms & security measures & & accurately determining & [55, 56] \\ \hline Collaborative Security & Collaborative between & Cooperative Security & & & \\  & devices & Algorithms, Distributed & & & \\  & devices & Ledger Technology & & & \\ \hline Human Factors & Usable security, user & Human-Centered & & & \\  & education & Design, Security & & & \\  & Training Programs & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._The selection of the Q-learning reinforcement model for the AI engine is mainly due to the unique and superior adaptability of the model. Thus, enabling the framework to respond effectively to zero-day threats or previously unseen threats by the model without requiring a large dataset of labeled security incidents. Unlike static rule-based systems, Q-learning facilitates continuous learning, allowing the security policies to evolve and improve based on real-time outcomes of actions taken.