Vulnerability Detection Methods Research Articles

UniBin: Assembly semantic-enhanced binary vulnerability detection without disassembly

The widespread reuse of open-source code amplifies the impact of vulnerabilities. Current vulnerability detection methods predominantly rely on binary code similarity comparisons, which involve disassembling to obtain assembly code or control flow graphs. These methods depend on specific disassembly tools and complex preprocessing, limiting their applicability and detection speed. This paper proposes UniBin, a vulnerability detection method based on the multi-layer Transformer encoder. By employing bidirectional LM, unidirectional LM, and sequence-to-sequence LM tasks on both binary and assembly code during the pre-training phase, UniBin learns richer semantic information from binary machine code, enabling efficient similarity comparison without disassembly and mitigating the limitations of disassembly. We cross-compile 55 widely used open-source C projects as datasets. After 52 hours of pre-training and 8 hours of fine-tuning, UniBin reaches an average accuracy of 98.3% in similarity detection across compilation conditions, outperforming the state-of-the-art method. For search tasks across optimization options with a pool size of 1000, the Recall@1 metric improves by 28.2% (from 67.9% to 87.1%). UniBin eliminates dependency on specific disassembly tools and improves end-to-end binary analysis speed by over 36%. In real-world vulnerability detection tasks, UniBin detects all vulnerability functions with the lowest false positive rate of 0.16%.

Research on Software Vulnerability Detection Methods Based on Deep Learning

This paper aims to investigate software vulnerability detection methods based on deep learning to address the ever-growing challenges in software security. With the rapid development of information technology, software vulnerabilities have become the primary targets of cyberattacks, posing severe threats to economic, military, and social security. By analyzing the limitations of existing software vulnerability detection methods, this paper explores the potential applications of deep learning technology in this field. Through a literature review, relevant theories of software vulnerabilities are introduced, including the concept, types, and impacts of vulnerabilities. Subsequently, detailed descriptions of deep learning-based vulnerability detection methods are provided, encompassing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and their variant LSTM, as well as the application of program slicing techniques in vulnerability detection. By evaluating and improving existing deep learning models, a novel deep learning-based vulnerability detection framework is proposed, with its workflow and technical principles elaborated. The proposed deep learning-based vulnerability detection method can effectively enhance the accuracy and efficiency of software vulnerability detection, reduce reliance on expert knowledge, and lower human resource costs. Experimental results demonstrate that this method performs exceptionally well on multiple datasets, indicating broad application prospects and significant research value. The deep learning-based software vulnerability detection method offers new insights and tools for addressing current software security challenges. In the future, with the continuous development and improvement of deep learning technology, this method will play an even more crucial role in the field of software vulnerability detection.

MVD-HG: multigranularity smart contract vulnerability detection method based on heterogeneous graphs

Smart contracts have significant losses due to various types of vulnerabilities. However, traditional vulnerability detection methods rely extensively on expert rules, resulting in low detection accuracy and poor adaptability to novel attacks. To address these problems, in this paper, deep learning methods are combined with smart contract vulnerability code detection approaches. syntax trees (ASTs), which are special isomorphic graph structures, are an important bridge between source code and graph neural networks. By learning the AST, the model can understand the semantics of the source code. Moreover, graph neural networks have an increasing ability to address complex heterogeneous graphs. Therefore, control flow graphs are fused with data flow graphs on the basis of the ASTs to build heterogeneous graphs with richer code semantics. Furthermore, multigranularity analysis of the vulnerability detection results is performed, including coarse-grained contract-level vulnerability detection and fine-grained line-level vulnerability detection. Through this multigranularity detection approach, vulnerabilities in contracts can be identified and analysed more comprehensively, providing a richer perspective and more solutions for vulnerability detection. The experimental results show that the proposed multigranularity vulnerability detection method based on heterogeneous graphs (MVD-HG) improves both the accuracy and range of the detected vulnerability types in contract-level vulnerability detection tasks; moreover, in the line-level vulnerability detection task, the MVD-HG model achieves significant results and addresses the shortcomings of existing methods. In addition, based on code generation methods used in related fields, a data enhancement method based on the source code is developed, which effectively expands the experimental dataset to address the reduced credibility of the results due to insufficient amounts of data.

A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies

The number of new vulnerabilities continues to rise significantly each year. Simultaneously, vulnerability databases have challenges in promptly sharing new security events with enough information to improve protections against emerging cyberattack vectors and possible exploits. In this context, several organizations adopt strategies to protect their data, technologies, and infrastructures from cyberattacks by implementing anticipatory and proactive approaches to their system security activities. To this end, vulnerability management systems play a crucial role in mitigating the impact of cyberattacks by identifying potential vulnerabilities within an organization and alerting cyber teams. However, the effectiveness of these systems, which employ multiple methods and techniques to identify weaknesses, relies heavily on the accuracy of published security events. For this reason, we introduce a discussion concerning existing vulnerability detection methods through an in-depth literature study of several research papers. Based on the results, this paper points out some issues related to vulnerability databases handling that impact the effectiveness of certain vulnerability identification methods. Furthermore, after summarizing the existing methodologies, this study classifies them into four approaches and discusses the challenges, findings, and potential research directions.

Vulnerability extraction and prediction method based on improved information gain algorithm.

More and more attention has been paid to computer security, and its vulnerabilities urgently need more sensitive solutions. Due to the incomplete data of most vulnerability libraries, it is difficult to obtain pre-permission and post-permission of vulnerabilities, and construct vulnerability exploitation chains, so it cannot to respond to vulnerabilities in time. Therefore, a vulnerability extraction and prediction method based on improved information gain algorithm is proposed. Considering the accuracy and response speed of deep neural network, deep neural network is adopted as the basic framework. The Dropout method effectively reduces overfitting in the case of incomplete data, thus improving the ability to extract and predict vulnerabilities. These experiments confirmed that the excellent F1 and Recall of the improved method reached 0.972 and 0.968, respectively. Compared to the function fingerprints vulnerability detection method and K-nearest neighbor algorithm, the convergence is better. Its response time is 0.12 seconds, which is excellent. To ensure the reliability and validity of the proposed method in the face of missing data, the reliability and validity of Mask test are verified. The false negative rate was 0.3% and the false positive rate was 0.6%. The prediction accuracy of this method for existing permissions reached 97.9%, and it can adapt to the development of permissions more actively, so as to deal with practical challenges. In this way, companies can detect and discover vulnerabilities earlier. In security repair, this method can effectively improve the repair speed and reduce the response time. The prediction accuracy of post-existence permission reaches 96.8%, indicating that this method can significantly improve the speed and efficiency of vulnerability response, and strengthen the understanding and construction of vulnerability exploitation chain. The prediction of the posterior permission can reduce the attack surface of the vulnerability, thus reducing the risk of breach, speeding up the detection of the vulnerability, and ensuring the timely implementation of security measures. This model can be applied to public network security and application security scenarios in the field of computer security, as well as personal computer security and enterprise cloud server security. In addition, the model can also be used to analyze attack paths and security gaps after security accidents. However, the prediction of post-permissions is susceptible to dynamic environments and relies heavily on the updated guidance of security policy rules. This method can improve the accuracy of vulnerability extraction and prediction, quickly identify and respond to security vulnerabilities, shorten the window period of vulnerability exploitation, effectively reduce security risks, and improve the overall network security defense capability. Through the application of this model, the occurrence frequency of security vulnerability time is reduced effectively, and the repair time of vulnerability is shortened.

Vulnerabilities and Security Patches Detection in OSS: A Survey

Over the past decade, Open Source Software (OSS) has experienced rapid growth and widespread adoption, attributed to its openness and editability. However, this expansion has also brought significant security challenges, particularly introducing and propagating software vulnerabilities. Despite the use of machine learning and formal methods to tackle these issues, there remains a notable gap in comprehensive surveys that summarize and analyze both vulnerability detection (VD) and security patch detection (SPD) in OSS. This article seeks to bridge this gap through an extensive survey that evaluates 127 technical studies published between 2014 and 2023, structured around the Vulnerability-Patch life cycle. We begin by delineating the six critical events that constitute the Vulnerability-Patch life cycle, leading to an in-depth exploration of the Vulnerability-Patch Ecosystem. We then systematically review the databases commonly used in VD and SPD, and analyze their characteristics. Subsequently, we examine existing VD methods, focusing on traditional and deep learning-based approaches. Additionally, we organize current security patch identification methods by kernel type and discuss techniques for detecting the presence of security patches. Based on our comprehensive review, we identify open research questions and propose future research directions that merit further exploration.

Securing Container Images through Automated Vulnerability Detection in Shift-Left CI/CD Pipelines

Integrating shift-left security practices and automated vulnerability detection in container images is imperative for modern software development, given the dynamics and vulnerability landscape. This crucial methodology emphasizes security from the initial stages of integration in container-based environments like Docker and Kubernetes. The paper examines containerization security challenges, including image vulnerabilities, insecure configurations, runtime risks, weak orchestration security, and supply chain weaknesses, while stressing compliance with regulatory rules. It explores how this automated approach leverages vulnerability detection methods integrated into Continuous Integration/Continuous Deployment (CI/CD) pipelines through static and dynamic analyses, vulnerability databases, and policy-enforcement mechanisms. Beyond identifying vulnerabilities in CI/CD pipelines, the paper outlines methods to avoid policy violations, mitigate vulnerable images, and prevent recurring practices. Importantly, it underscores the continuous enforcement and remediation of policies and security standards. Security teams must invest efforts in developing policies, automated executions, and remediation procedures, fostering cross-departmental collaboration. In essence, this proactive stance aims to enhance software security, reduce risks, and improve adherence in containerized ecosystems, making it an indispensable component of modern software development.

A vulnerability detection framework by focusing on critical execution paths

Context:Vulnerability detection is critical to ensure software security, and detecting vulnerabilities in smart contract code is currently gaining massive attention. Existing deep learning-based vulnerability detection methods represent the code as a code structure graph and eliminate vulnerability-irrelevant nodes. Then, they learn vulnerability-related code features from the simplified graph for vulnerability detection. However, this simplified graph struggles to represent relatively complete structural information of code, which may affect the performance of existing vulnerability detection methods. Objective:In this paper, we present a novel Vulnerability Detection framework based on Critical Execution Paths (VDCEP), which aims to improve smart contract vulnerability detection. Method:Firstly, given a code structure graph, we deconstruct it into multiple execution paths that reflect rich structural information of code. To reduce irrelevant code information, a path selection strategy is employed to identify critical execution paths that may contain vulnerable code information. Secondly, a feature extraction module is adopted to learn feature representations of critical paths. Finally, we feed all path feature representations into a classifier for vulnerability detection. Also, the feature weights of paths are provided to measure their importance in vulnerability detection. Results:We evaluate VDCEP on a large dataset with four types of smart contract vulnerabilities. Results show that VDCEP outperforms 14 representative vulnerability detection methods by 5.34%–60.88% in F1-score. The ablation studies analyze the effects of our path selection strategy and feature extraction module on VDCEP. Moreover, VDCEP still outperforms ChatGPT by 34.46% in F1-score. Conclusion:Compared to existing vulnerability detection methods, VDCEP is more effective in detecting smart contract vulnerabilities by utilizing critical execution paths. Besides, we can provide interpretable details about vulnerability detection by analyzing the path feature weights.

A vulnerability detection framework with enhanced graph feature learning

Vulnerability detection in smart contracts is critical to secure blockchain systems. Existing methods represent the bytecode as a graph structure and leverage graph neural networks to learn graph features for vulnerability detection. However, these methods are limited to handling the long-range dependencies between nodes. This means that they might focus on learning local node feature while ignoring global node information. In this paper, we propose a novel vulnerability detection framework with Enhanced Graph Feature Learning (EGFL), which aims to extract the global node information and utilize it to improve vulnerability detection in smart contracts. Specifically, we first represent the bytecode as a Control Flow Graph (CFG). To extract global node information, EGFL constructs a linear node feature matrix from CFG, and uses the feature-aware and relationship-aware modules to handle long-range dependencies between nodes. Meanwhile, a graph neural network is adopted to extract the local node feature from CFG. Subsequently, we fuse the global node information and local node feature to generate an enhanced graph feature for capturing more vulnerability features. We evaluate EGFL on the benchmark dataset with six types of smart contract vulnerabilities. Results show that EGFL outperforms fourteen state-of-the-art vulnerability detection methods by 10.83%–60.28% in F1 score.

A vulnerability detection method for IoT protocol based on parallel fuzzy algorithm

The Internet of Things communication protocol is prone to security vulnerabilities when facing increasing types and scales of network attacks, which can affect the communication security of the Internet of Things. It is crucial to effectively detect these vulnerabilities in order to improve the security of IoT communication protocols and promptly fix them. Therefore, this study proposes a distributed IoT communication protocol vulnerability detection method based on an improved parallelized fuzzy testing algorithm. Firstly, based on design principles and by comparing different communication protocols, a communication architecture for the distribution network's Internet of Things was constructed, and the communication protocols were formalized and decomposed. Next, preprocess the vulnerability detection samples, and then use genetic algorithm to improve the parallelized fuzzy testing algorithm to perform vulnerability detection. Through this improved algorithm, the missed detection rate and false detection rate can be effectively reduced, thereby improving the security of IoT communication protocols. The experimental results show that the highest missed detection rate of this method is only 4.0 %, and the false detection rate is low, with high detection efficiency. This indicates that the method has good performance and reliability in detecting vulnerabilities in IoT communication protocols.

Reentrancy vulnerability detection based on graph convolutional networks and expert patterns under subspace mapping

Smart contracts with automatic execution capability provide a vast development space for transactions in Blockchain. However, due to the vulnerabilities in smart contracts, Blockchain has suffered huge economic losses, which greatly undermines people’s trust in Blockchain and smart contracts. In this paper, we explore a vulnerability detection method based on graph neural networks and combine both contract source code and opcode. The structure of the method consists of four modules, i.e., preprocessing, subspace mapping, feature extraction, and detection modules. In the feature mapping module, we use a multi-subspace mapping approach to explore the impact of different subspace mappings on the detection method. For reentrancy vulnerability, we conducted extensive experiments. The experiments prove that our method achieves 95% accuracy and 94% F1-Score on average.

VulMPFF: A Vulnerability Detection Method for Fusing Code Features in Multiple Perspectives

Source code vulnerabilities are one of the significant threats to software security. Existing deep learning-based detection methods have proven their effectiveness. However, most of them extract code information on a single intermediate representation of code (IRC), which often fails to extract multiple information hidden in the code fully, significantly limiting their performance. To address this problem, we propose VulMPFF, a vulnerability detection method that fuses code features under multiple perspectives. It extracts IRC from three perspectives: code sequence, lexical and syntactic relations, and graph structure to capture the vulnerability information in the code, which effectively realizes the complementary information of multiple IRCs and improves vulnerability detection performance. Specifically, VulMPFF extracts serialized abstract syntax tree as IRC from code sequence, lexical and syntactic relation perspective, and code property graph as IRC from graph structure perspective, and uses Bi-LSTM model with attention mechanism and graph neural network with attention mechanism to learn the code features from multiple perspectives and fuse them to detect the vulnerabilities in the code, respectively. We design a dual-attention mechanism to highlight critical code information for vulnerability triggering and better accomplish the vulnerability detection task. We evaluate our approach on three datasets. Experiments show that VulMPFF outperforms existing state-of-the-art vulnerability detection methods (i.e., Rats, FlawFinder, VulDeePecker, SySeVR, Devign, and Reveal) in Acc and F1 score, with improvements ranging from 14.71% to 145.78% and 152.08% to 344.77%, respectively. Meanwhile, experiments in the open-source project demonstrate that VulMPFF has the potential to detect vulnerabilities in real-world environments.

Python source code vulnerability detection with named entity recognition

Vulnerabilities within source code have grown over the last 20 years to become a common threat to systems and networks. As the implementation of open-source software continues to develop, more unknown vulnerabilities will exist throughout system networks. This research proposes an enhanced vulnerability detection method specific to Python source code that utilizes pre-trained, BERT-based transformer models to apply tokenization, embedding, and named entity recognition (a natural language processing technique). The use of named entity recognition not only allows for the detection of potential vulnerabilities, but also for the classification of different vulnerability types. This research uses the publicly available CodeBERT, RoBERTa, and DistilBERT models to fine-tune for the downstream task of token classification for six different common weakness enumeration specifications. The results achieved in this research outperform previous Python-based vulnerability detection methods and demonstrate the effectiveness of applying named entity recognition to enhance the overall research into Python source code vulnerabilities.

DA-GNN: A smart contract vulnerability detection method based on Dual Attention Graph Neural Network

A smart contract is an automated computer program based on blockchain technology. In recent years, the security incidents of smart contracts have caused serious economic losses. However, existing smart contract vulnerability detection methods rely on fixed expert rules, resulting in reduced detection accuracy and scalability. Therefore, addressing the issues of low accuracy in traditional smart contract vulnerability detection methods and the insufficient feature extraction in neural network-based approaches for smart contracts, this paper introduces an intelligent contract vulnerability identification method, Dual Attention Graph Neural Network (DA-GNN). Firstly, DA-GNN transforms the operation code sequence of nodes in the smart contract Control Flow Graph (CFG) into a feature matrix of semantic features and relationships between nodes based on the five types of instructions we propose. Secondly, our proposed dual attention mechanism introduces node semantic features and relationship features between nodes into the GAT to achieve node embedding updates. The updated graph node information is fused through self-attention mechanism to obtain the graph features. Then, the classification and prediction of vulnerabilities are achieved through the classification module. Finally, we evaluated our method on 17,670 real smart contracts. The experimental results show that the precision in detecting integer overflow vulnerabilities, self-destruct vulnerabilities, and transaction sequence dependency vulnerabilities reaches 72.17%, 67.03%, and 73.66%, respectively.

Smart Contract Vulnerability Detection Based on Multi-Scale Encoders

Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, to solve the limitations of current deep learning-based vulnerability detection methods in extracting various code critical features, using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-Scale Encoder Vulnerability Detection (MEVD) approach to hit well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model’s attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

MVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (<i>i.e</i>., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design <i>mVulPreter</i>, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results&#x0027; interpretation only by the model itself. We evaluate <i>mVulPreter</i> on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that <i>mVulPreter</i> outperforms existing state-of-the-art vulnerability detection approaches (<i>i.e</i>., <i>Checkmarx</i>, <i>FlawFinder</i>, <i>RATS</i>, <i>TokenCNN</i>, <i>StatementLSTM</i>, <i>SySeVR</i>, and <i>Devign</i>).

A New Smart Contract Anomaly Detection Method by Fusing Opcode and Source Code Features for Blockchain Services

Digital assets involved in smart contracts are on the rise. Security vulnerabilities in smart contracts have resulted in significant losses for the blockchain community. Existing smart contract vulnerability detection techniques have been typically single-purposed and focused only on the source code or opcode of contracts. This paper presents a new smart contract vulnerability detection method, which extracts features from different levels of smart contracts to train machine learning models for effective detection of vulnerabilities. Specifically, we propose to extract 2-gram features from the opcodes of smart contracts and token features from the source code using a pre-trained CodeBERT model, thereby capturing the semantic information of smart contracts at different levels. The 2-gram and token features are separately aggregated and then fused and input into machine-learning models to mine the vulnerability features of contracts. Over 10,266 smart contracts are used to verify the proposed method. Widespread reentrancy, timestamp dependence, and transaction-ordering dependence vulnerabilities are considered. Experiments show the fused features can help significantly improve smart contract vulnerability detection compared to the single-level features. The detection accuracy is as high as 98%, 98% and 94% for the three vulnerabilities, respectively. The average detection time is 0.99 second per contract, indicating the proposed method is suitable for automatic batch detection of vulnerabilities in smart contracts.

A software vulnerability detection method based on deep learning with complex network analysis and subgraph partition

A software vulnerability detection method based on deep learning with complex network analysis and subgraph partition

A Smart Contract Vulnerability Detection Method Based on Multimodal Feature Fusion and Deep Learning

With the proliferation of blockchain technology in decentralized applications like decentralized finance and supply chain and identity management, smart contracts operating on a blockchain frequently encounter security issues such as reentrancy vulnerabilities, timestamp dependency vulnerabilities, tx.origin vulnerabilities, and integer overflow vulnerabilities. These security concerns pose a significant risk of causing substantial losses to user accounts. Consequently, the detection of vulnerabilities in smart contracts has become a prominent area of research. Existing research exhibits limitations, including low detection accuracy in traditional smart contract vulnerability detection approaches and the tendency of deep learning-based solutions to focus on a single type of vulnerability. To address these constraints, this paper introduces a smart contract vulnerability detection method founded on multimodal feature fusion. This method adopts a multimodal perspective to extract three modal features from the lifecycle of smart contracts, leveraging both static and dynamic features comprehensively. Through deep learning models like Graph Convolutional Networks (GCNs) and bidirectional Long Short-Term Memory networks (bi-LSTMs), effective detection of vulnerabilities in smart contracts is achieved. Experimental results demonstrate that the proposed method attains detection accuracies of 85.73% for reentrancy vulnerabilities, 85.41% for timestamp dependency vulnerabilities, 83.58% for tx.origin vulnerabilities, and 90.96% for integer Overflow vulnerabilities. Furthermore, ablation experiments confirm the efficacy of the newly introduced modal features, highlighting the significance of fusing dynamic and static features in enhancing detection accuracy.