## 7 Discussion and Future Directions  
This survey underscores the multifaceted nature of blockchain security, emphasizing that comprehensive security measures require structured analysis of vulnerabilities and methodological clarity in their detection. The proposed taxonomies offer practical frameworks that help blockchain practitioners, developers, and cybersecurity analysts categorize attackssystematically and design targeted mitigation strategies. Practically, this means smart contract developers can leverage existing auditing frameworks (e.g., Slither or Mythril) in alignment with the vulnerabilities identified by our taxonomy, potentially increasing the effectiveness and efficiency of vulnerability detection and mitigation processes.  
More broadly, the taxonomy can serve as a foundational tool to inform real-world blockchain security policies by enabling system designers and security teams to map out threat surfaces layer by layer, assess which operational components are at risk, and define security controls that directly correspond to those architectural layers. This structured alignment between vulnerabilities and architectural responsibilities can help formalize internal audit procedures, incident response planning, and regulatory compliance strategies in both enterprise and public blockchain settings.  
An important consideration is the adaptability of our taxonomies to evolving blockchain paradigms. Future iterations of our taxonomies can explicitly address emerging threats and technological advancements such as quantum computing and artificial intelligence-driven vulnerabilities. Quantum-resistant cryptographic schemes and consensus algorithms will become increasingly critical as quantum computing capabilities advance. Furthermore, as machine learning, particularly federated learning (FL), becomes more prevalent, its application to decentralized, privacy-preserving security analytics emerges as a promising research direction. Another critical avenue is the integration of zero-trust security paradigms to enable continuous authentication and verification within blockchain networks, addressing the growing sophistication of attacks.  
The emergence of large language models (LLMs) also opens new possibilities for automated, sophisticated code analysis and real-time vulnerability detection. However, adversaries could leverage similar models for vulnerability identification and exploitation. This duality emphasizes the urgent need for robust, explainable, and adaptive ML-based defense mechanisms. Addressing these challenges will necessitate continued interdisciplinary collaboration among blockchain specialists, cybersecurity researchers, cryptographers, quantum computing experts, and policymakers. Such collaboration will ensure that blockchain ecosystems remain resilient against current vulnerabilities and future threats.  
Lastly, although we did not perform a comparative empirical study, our layer-based framework can help identify which architectural layers may be more susceptible in specific blockchain architectures. For instance, platforms using DAG-based structures such as IOTA may face unique data-layer vulnerabilities, while PoW-based systems are more exposed to consensus-layer threats such as selfish mining. Such insights offer a strong starting point for future research into architecture-specific threat modeling.