# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 6 Discussion
### Potential Advantages of Dynamic Honeypots in Internet of Things over Adaptative Honeypots  
The debate over the effectiveness of evolutionary dynamic honeypots versus adaptive honeypots in IoT security highlights their distinct strengths and the nuanced contexts in which each can excel. Evolutionary dynamic honeypots bring a revolutionary approach to deception, leveraging phase-based cycles of real and fake services to create unpredictable environments. This unpredictability significantly increases the challenge for attackers, disrupting their ability to exploit systems. For instance, experiments with HoneyShell demonstrated the power of evolutionary dynamics. By alternating between phases, HoneyShell refined credential requirements and incorporated realistic responses based on prior attacker interactions. These adjustments not only maintained authenticity but also enabled the capture of deeper insights into attacker behavior. This phase-based adaptation is a hallmark of evolutionary systems, where constantly switching states reduces the average attack success rate and creates a security environment that outperforms static methods. HoneyShell's deliberate refinements--such as enhanced user credentials, commands, and responses--offer a prime example of how such systems convincingly mimic real environments, making it harder for attackers to identify and exploit honeypots.  
However, one might argue that while evolutionary systems excel in their broad-spectrum deterrence and long-term adaptability, they may not always be the best choice for scenarios requiring immediate, precise interaction. This is where adaptive honeypots come into play, offering unparalleled real-time responsiveness. Adaptive honeypots are designed to tailor their responses based on the attacker's behavior in the moment, ensuring sustained engagement and enhancing the illusion of interacting with an authentic IoT device. This ability to react dynamically is particularly effective against novel attack strategies or unknown vulnerabilities, where pre-programmed evolution might lag behind emerging threats. Moreover, adaptive honeypots are well-suited for resource-constrained IoT setups, where the complexity of evolutionary systems may not be practical to implement.  
The strengths of both evolutionary dynamic and adaptive honeypots lie in their complementary approaches. Are we aiming to confuse attackers over time, as seen in HoneyShell's strategic evolution, or is the goal to dive deeply into specific interactions to uncover new insights in real time? The impact of these systems, whether through the dynamism of evolutionary cycles or the immediacy of adaptive engagement, underscores their vital roles in modern IoT security. By blending these approaches, we can envision a future where the balance of deception and responsiveness creates a more robust and versatile defense against ever-evolving cyber threats.  
The analysis reveals consistent efforts to balance realism and scalability across IoT honeypot systems. Both low- and high-interaction honeypots struggle with generating believable traffic, while hybrid approaches aim to merge their strengths. Machine learning techniques are widely adopted but diverge in trade-offs: supervised learning achieves high accuracy but requires labeled data, whereas reinforcement learning adapts dynamically at higher computational costs. Key challenges include managing large data volumes, real-world deployment complexities of high-interaction systems, and environmental sensitivity that delays responses to new attack patterns.  
Current methods remain limited by static configurations and resource intensity. Future work should prioritize lightweight machine learning integration to address deployability constraints. Cross-platform standardization could enhance threat intelligence sharing, while advanced adaptive models may improve autonomy in dynamic environments. These directions align with the observed gaps in existing systems, such as the need for real-time adaptability and efficient resource use.