VulDeePecker: A Deep Learning-Based System for Vulnerability Detection (1801.01681v1)

Abstract: The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with.

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

VulDeePecker is a deep learning-based vulnerability detection system aimed at automating the identification of vulnerabilities in software, thereby addressing the limitations of existing systems that typically require intensive manual feature definition and incur high false negatives. The primary goal of VulDeePecker is to detect software vulnerabilities without heavily relying on human experts to define system features, thereby simplifying and enhancing the efficiency of the vulnerability detection process.

Background and Motivations

Traditional approaches to vulnerability detection are hampered by two main challenges: the need for extensive manual feature definition by experts and high false negative rates. Human-defined features are often subjective, labor-intensive, and variable in quality. Additionally, many existing solutions fail to detect a significant number of vulnerabilities, leading to substantial false negatives. These limitations underscore the need for an automated system capable of detecting vulnerabilities accurately without manual feature engineering.

Main Contributions

1. Deep Learning for Vulnerability Detection

The paper initiates the use of deep learning for automatic vulnerability detection. Unlike traditional methods that rely on predefined rules or features, VulDeePecker leverages a deep learning model, specifically Bidirectional Long Short-Term Memory (BLSTM) networks, to automatically learn patterns of vulnerabilities from a large dataset. This approach is guided by principles for representation, granularity, and network selection suitable for vulnerability detection.

• Representation: Programs are represented using "code gadgets," which are small fragments of semantically related lines of code.
• Granularity: The system analyzes code at a finer granularity than entire functions or files, focusing on code gadgets to pinpoint vulnerabilities precisely.
• Network Selection: The choice of BLSTM networks allows VulDeePecker to consider both past and future contexts, which is critical in understanding code semantics for vulnerability detection.

2. Dataset Creation and Labeling

A significant contribution of the paper is the creation of a comprehensive dataset for evaluating deep learning-based vulnerability detection systems. This dataset is derived from the National Vulnerability Database (NVD) and the Software Assurance Reference Dataset (SARD) and includes detailed sample labeling.

• The dataset consists of 61,638 code gadgets, among which 17,725 are labeled as vulnerable.
• Code gadgets were labeled using a combination of automated tools and manual verification to ensure accuracy.

3. Evaluation and Findings

The evaluation of VulDeePecker involves several experiments to address key research questions:

• Effectiveness Across Multiple Vulnerability Types: The system effectively handles multiple types of vulnerabilities simultaneously, demonstrating its versatility.
• Impact of Human Expertise: Incorporating human-selected library/API function calls can improve the detection system's precision and recall.
• Comparison with Other Systems: VulDeePecker significantly outperforms traditional static analysis tools and recent code similarity-based approaches. Notably, it has lower false negative rates while maintaining reasonable false positive rates.

Experimental Results

VulDeePecker was tested against 19 popular C/C++ open-source projects. It demonstrated superior performance metrics (e.g., F1-score, precision, recall) compared to other tools like Flawfinder, RATS, Checkmarx, VUDDY, and VulPecker. Additionally, VulDeePecker was able to detect several vulnerabilities that were not identified or were missed by these other tools, further showcasing its potency.

Implications and Future Directions

Practically, VulDeePecker reduces the labor involved in manual vulnerability detection, thus streamlining the process. Theoretically, it shows that deep learning can be effectively applied to security-related tasks, marking a significant stride forward in the field.

Future research directions may include broadening the range of programming languages supported by VulDeePecker, enhancing the underlying dataset with more diverse types of vulnerabilities, and integrating control flow analysis to complement the current data flow analysis. There is also potential for refining the system to detect 0-day vulnerabilities more efficiently.

Conclusion

VulDeePecker represents a major step towards automating vulnerability detection by harnessing the power of deep learning. While there are areas for improvement and expansion, this paper lays a strong foundation and highlights the significant benefits and potential of deep learning-driven approaches in enhancing software security.

For reference, the VulDeePecker dataset is made available for public use, fostering further research and development in this pivotal area of cybersecurity.