## 5 Vulnerability Detection
### A Systematic Taxonomy of Vulnerability Detection  
Our taxonomy provides a coherent framework, allowing a nuanced comparison of methods and identification of gaps in current research. In Figure 7, we present a systematic taxonomy that classifies research papers focused on blockchain vulnerability detection. This taxonomy was developed through a structured process that involved several key steps, ensuring a comprehensive and accurate categorization of the state-of-the-art literature in this field.  
The classification emerged from a rigorous, multi-step analytical process.  
Paper Identification: Initially we performed comprehensive keyword searches in academic databases to gather relevant state-of-the art literature. This step ensured that all significant and recent contributions were included.  
Understanding the Core Problem: Each identified paper was critically examined to identify its primary research problem. This involved recognizing whether the focus was on specific vulnerabilities, novel detection methodologies, or particular domains within blockchain security.  
Identifying Key Features and Approaches: In this step, we extracted the methodologies and techniques used by the papers. Key methodologies such as deep learning, static analysis, fuzzing, or hybrid approaches were extracted. This allowed us to map techniques clearly to identified vulnerabilities and research problems.  
Grouping Based on Similar Features: Papers were then grouped together based on shared characteristics and approaches. This clustering process ensured that each category represented a distinct theme or focus. This facilitated a detailed comprehensive analysis.  
Formalizing the Taxonomy: Clusters were systematically organized into a coherent taxonomy, clearly delineating methodological boundaries and ensuring each paper was categorized based on its primary contributions.This organization reflects the common themes and features identified in the previous steps.  
Figure 7: Systematic taxonomy creation for blockchain vulnerability detection research papers.  
KeyAspects of the Taxonomy  
The taxonomy is organized around several key aspects, which provide insights into current research trends and help identify potential gaps.  
1. Detection Methods:  
Deep Learning and Machine Learning: Papers using neural networks, graph neural networks, attention models, or other machine learning techniques for vulnerability detection.  
Static Program Analysis: Research focusing on static code analysis, program analysis, and formal methods to identify vulnerabilities in smart contracts and blockchain applications.  
Fuzzing: Studies that utilize fuzz testing techniques to uncover vulnerabilities by inputting random data into blockchain systems.  
2. Specific Problem Focus:  
Vulnerability Repair and Fraud Detection: Research dedicated to the automated repair of detected vulnerabilities and the identification of fraudulent transactions within blockchain networks.  
Datasets and Evaluation: Papers that create or leverage large datasets for evaluating the performance and robustness of smart contract analysis techniques.  
3. Comprehensive Surveys and Reviews: These works provide broad overviews of the field, classifying existing tools and methodologies while outlining future research directions in blockchain vulnerability detection.  
4. Domain-Specific Enhancements: This category includes research focused on enhancing security in specific domains, such as the Internet of Things (IoT) or blockchain consensus mechanisms, to address unique challenges in those areas.  
In summary, this taxonomy not only clarifies the landscape of blockchain vulnerability detection research but also offers guidance for future studies by highlighting current trends and identifying areas that require further exploration. As illustrated in Figure 7, the classification provides a structured framework that can help researchers select suitable techniques and address potential gaps in the literature.