# Defense and Security Mechanisms in the Internet of Things: A Review
## 6 Discussion
#### Research Limitations and Future Directions  
While thoroughly observing defense and security in networks of connected devices, this research has several limitations. For starters, the rapid advancement of IoT technology and its security models makes it practically impossible to keep up with the unfolding events, a task that may not be achievable. Despite our efforts to include the most relevant and up-to-date research, this review may not cover emerging mechanisms. In this work, we reviewed the relevant methods from a theoretical perspective. Most surveyed methods do not consider practical implementation challenges regarding IoT devices' scalability, computational overheads, and constrained energy resources. Therefore, this indicates a gap in the studies conducted, and further investigations should analyse the practical deployment of these mechanisms in realistic IoT scenarios.  
Another significant limitation is the lack of thorough benchmarking regarding the IDS and anomaly detection frameworks considered. Typically, there is a lack of standard datasets or uniform metrics for comparing various approaches and drawing definitive conclusions about the relative merit of the competing methods. This work finally concentrates on the defense mechanisms, not extensively considering interdisciplinary factors, such as user behaviour, organisation policy, or economic factors that may influence IoT security.  
Also, we can highlight here some directions for future work:  
* Future research should emphasise real-world testing of IDSs and anomaly detection frameworks, addressing false positives, adaptability, and long-term performance in dynamic IoT environments.
* The development of consistent evaluation standards and publicly available datasets for the mechanisms in IoT security will help ensure fairness in innovation comparisons.
* When designing low-power smart devices, creating power-efficient and computationally easy-to-perform security schemes is essential.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Solution** & **Type/Application/Advantages/Limitations** \\ \hline Deep Learning-Powered Authentication & • Authentication and Key Agreement Protocol * IoT and LTE Systems * Enhanced detection rates, resistance to DoS and MITM attacks * Scalability and resource limitations in satellite-based systems \\ \hline Advanced Hybrid Anomaly Detection & • Anomaly Detection System * MANET-IoT Sensors * High accuracy using hybrid optimisation algorithms * Requires significant computational power and training complexity \\ \hline Visual Cryptography-Based Authentication & • Authentication Protocol * IoT Cloud Environments * Strong data confidentiality, integrity, and authenticity * Requires additional infrastructure for secure ticket generation \\ \hline Quantum-Secure Key Agreement Protocol & • Key Agreement Protocol * Medical IoT Environments * Resistance to quantum attacks, strong anonymity * High complexity and implementation cost in current IoT systems \\ \hline AI-Enhanced IDS with Ensemble Learning & • Intrusion Detection System * IoT in Critical Infrastructures * Superior threat detection metrics (for example, F1 score above 99\%) * High data demands, potential overfitting on limited datasets \\ \hline \hline \end{tabular}
\end{table}
Table 10: Cont.