WebAssembly (Wasm) is a web standard that defines a binary instruction format serving as a portable compilation target for high-level programming languages. As a cornerstone design feature, code porting empowers developers to migrate code across platforms without extensive redevelopment. However, we observe that certain native code exhibits functional discrepancies compared to its Wasm counterpart. These discrepancies impede migration by causing functional alterations, generating misleading messages, and forcing platform-specific workarounds.

Automated commit message generation, which aims at generating natural language description from code change, allows developers to focus more on project maintenance and management. To ensure the quality of commit messages, most projects constrain their style and adopt the conventional commit specification. Conventional commit message generation has been significantly benefited from recent progress in Large Language Models (LLMs). However, previous approaches typically rely on only one or two type of information for the generation, ignoring a wide range of context information. Moreover, they often extract the context in a coarse-grained manner, missing critical details.To address this limitation, We propose CCMG, a novel hierarchical context-augmentedConventionalCommitMessageGeneration framework, which incorporates project-agnostic and project-specific context. For project agnostic context, CCMG retrieves and refines the relevant commits to align conventional commit specification from large-scale corpus. For project-specific context, CCMG provides a wide range of software context information from the perspective of project, code, and style. Finally, CCMG designs two-stage prompt strategy to focus on conventional message inference and commit type adaptation. Compared with the state-of-the-art LLM-based approaches (i.e., OMG and OMEGA), experiment results show that CCMG achieves an average improvement of 31.45% based on human evaluation in commit message generation and improves accuracy by 21.00% and F1 score by 20.88% in commit type classification.

With the advancement of WebAssembly, abbreviated as Wasm, various memory bugs and undefined behaviors have emerged, leading to security issues that affect usability and portability. Existing methods struggle to detect these problems in Wasm binaries due to challenges associated with binary instrumentation and the difficulty of defining legal memory bounds.While sanitizers combined with fuzzing are recognized as effective means for identifying bugs, current Wasm sanitizers necessitate compile-time instrumentation, rendering them unsuitable for practical scenarios where only binaries are accessible. In this paper, we propose WBSan, the first Wasm binary sanitizer employing static analysis and Wasm binary instrumentation to detect memory bugs and undefined behaviors. We develop distinct instrumentation patterns tailored for each type of bug and introduce Wasm shadow memory to address complex memory bugs. Our results reveal that WBSan achieves a 16.8% false detection rate, outperforming current Wasm binary checkers and native sanitizers in detecting memory bugs and undefined behaviors. Furthermore, when compared with the binary-only fuzzer, WBSan uncovers more crashes and achieves greater code coverage.

Recent methods have demonstrated that machine learning (ML) based static malware detection models are vulnerable to adversarial attacks. However, the generated malware often fails to generalize to production-level anti-malware software (AMS), as they usually involve multiple detection methods. This calls for universal solutions to the problem of malware variants generation. In this work, we demonstrate how the proposed method, MalwareTotal, has allowed malware variants to continue to abound in ML-based, signature-based, and hybrid anti-malware software. Given a malicious binary, we develop sequential bypass tactics that enable malicious behavior to be concealed within multi-faceted manipulations. Through 12 experiments on real-world malware, we demonstrate that an attacker can consistently bypass detection (98.67%, and 100% attack success rate against ML-based methods EMBER and MalConv, respectively; 95.33%, 92.63%, and 98.52% attack success rate against production-level anti-malware software ClamAV, AMS A, and AMS B, respectively) without modifying the malware functionality. We further demonstrate that our approach outperforms state-of-the-art adversarial malware generation techniques both in attack success rate and query consumption (the number of queries to the target model). Moreover, the samples generated by our method have demonstrated transferability in the real-world integrated malware detector, VirusTotal. In addition, we show that common mitigation such as adversarial training on known attacks cannot effectively defend against the proposed attack. Finally, we investigate the value of the generated adversarial examples as a means of hardening victim models through an adversarial training procedure, and demonstrate that the accuracy of the retrained model against generated adversarial examples increases by 88.51 percentage points.

Code authorship attribution has been an interesting research problem for decades. Recent studies have revealed that existing methods for code authorship attribution suffer from weak robustness. Under the influence of small perturbations added by the attacker, the accuracy of the method will be greatly reduced. As of now, there is no code authorship attribution method capable of effectively handling such attacks. In this paper, we attribute the weak robustness of code authorship attribution methods to dataset bias and argue that this bias can be mitigated through adjustments to the feature learning strategy. We first propose a robust code authorship attribution feature combination framework, which is composed of only simple shallow neural network structures, and introduces controllability for the framework in the feature extraction by incorporating expert knowledge. Experiments show that the framework has significantly improved robustness over mainstream code authorship attribution methods, with an average drop of 23.4% (from 37.8% to 14.3%) in the success rate of targeted attacks and 25.9% (from 46.7% to 20.8%) in the success rate of untargeted attacks. At the same time, it can also achieve results comparable to mainstream code authorship attribution methods in terms of accuracy.

Duck typing is a flexible programming style in dynamic languages, enabling the achievement of complex behaviors using less code. The use of duck typing is currently widespread; however, the question is whether its use in code is truly safe. In fact, improper use of duck typing may introduce unexpected security threats. In this paper, we reveal another side of duck typing, showing how it can exacerbate the impact of deserialization vulnerabilities and expand the range of attack options for attackers. We present three cases of duck typing misuse and theoretically demonstrate how such misuse can expand the attack surface of deserialization vulnerabilities. Additionally, we design a static analysis tool, GrayDuck, to construct a Class Relation Graph (CRG) that clearly delineates the range of classes accessible through each deserialization operation and identify instances of duck typing misuse along with the associated attack surfaces so that to assess the potential harm. We utilized this tool to scan 5 Python programs known to have real deserialization vulnerabilities, detecting 7 issues of deserialized object duck typing misuse and calculating the corresponding expansions of the attack surfaces.

Binary Code Similarity Detection (BCSD) is a fundamental binary analysis technique in the area of software security. Recently, advanced deep learning algorithms are integrated into BCSD platforms to achieve superior performance on well-known benchmarks. However, real-world large programs embed more complex diversities due to different compilers, various optimization levels, multiple architectures and even obfuscations. Existing BCSD solutions suffer from low accuracy issues in such complicated real-world application scenarios. In this paper, we propose BinCola, a novel Transformer-based dual diversity-sensitive contrastive learning framework that comprehensively considers the diversity of compiler options and candidate functions in the real-world application scenarios and employs the attention mechanism to fuse multi-granularity function features for enhancing generality and scalability. BinCola simultaneously compares multiple candidate functions across various compilation option scenarios to learn the differences caused by distinct compiler options and different candidate functions. We evaluate BinCola's performance in a variety of ways, including binary similarity detection and real-world vulnerability search in multiple application scenarios. The results demonstrate that BinCola achieves superior performance compared to state-of-the-art (SOTA) methods, with improvements of 2.80%, 33.62%, 22.41%, and 34.25% in cross-architecture, cross-optimization level, cross-compiler, and cross-obfuscation scenarios, respectively.

Third-party libraries (TPLs) have become an essential component of software, accelerating development and reducing maintenance costs. However, breaking changes often occur during the upgrades of TPLs and prevent client programs from moving forward. Semantic versioning (SemVer) has been applied to standardize the versions of releases according to compatibility, but not all releases follow SemVer compliance. Lots of work focuses on SemVer compliance in ecosystems such as Java and JavaScript beyond Golang (Go for short). Due to the lack of tools to detect breaking changes and dataset for Go, developers of TPLs do not know if breaking changes occur and affect client programs, and developers of client programs may hesitate to upgrade dependencies in terms of breaking changes. To bridge this gap, we conduct the first large-scale empirical study in the Go ecosystem to study SemVer compliance in terms of breaking changes and their impact. In detail, we propose GoSVI (Go Semantic Versioning Insight) to detect breaking changes and analyze their impact by resolving identifiers in client programs and comparing their types with breaking changes. Moreover, we collect the first large-scale Go dataset with a dependency graph from GitHub, including 124K TPLs and 532K client programs. Based on the dataset, our results show that 86.3% of library upgrades follow SemVer compliance and 28.6% of no-major upgrades introduce breaking changes. Furthermore, the tendency to comply with SemVer has improved over time from 63.7% in 2018/09 to 92.2% in 2023/03. Finally, we find 33.3% of downstream client programs may be affected by breaking changes. These findings provide developers and users of TPLs with valuable insights to help make decisions related to SemVer.

Intelligent deep learning-based models have made significant progress for automated source code semantics embedding, and current research works mainly leverage natural language-based methods and graph-based methods. However, natural language-based methods do not capture the rich semantic structural information of source code, and graph-based methods do not utilize rich distant information of source code due to the high cost of message-passing steps. In this article, we propose a novel interpretable model, called graph tensor convolution neural network (GTCN), to generate accurate code embedding, which is capable of comprehensively capturing the distant information of code sequences and rich code semantics structural information. First, we propose to utilize a high-dimensional tensor to integrate various heterogeneous code graphs with node sequence features, such as control flow, data flow. Second, inspired by the current advantages of graph-based deep learning and efficient tensor computations, we propose a novel interpretable graph tensor convolution neural network for learning accurate code semantic embedding from the code graph tensor. Finally, we evaluate three popular applications on the GTCN model: variable misuse detection, source code prediction, and vulnerability detection. Compared with current state-of-the-art methods, our model achieves higher scores with respect to the top-1 accuracy while costing less training time.

Recommendations based on prediction of user preferences from partial information are widely used in various applications. However, recommendations using smart devices have some challenges related to limited data device resources, data sparsity, and data privacy. Since there are many multidimensional data in smart devices, recommendations may collect a large amount of user private data. In this article, we study privacy-preserving recommendations with high-dimensional tensor data in smart devices. First, we propose a federated tensor completion scheme to infer the user’s preferences and we prove that this scheme satisfies the differential privacy guarantee. Our scheme consists of a global update and a local update, which reduce information exposure and guarantee local data privacy. Second, we mathematically analyze the privacy and utility of the proposed algorithm. Third, we provide empirical evaluations on synthetic data sets and real-world data sets. Results show that our scheme has a low recovery error and provides strong privacy protection.

Abstract Using multi‐task learning to extract code features can effectively increase the information of the features. However, the existing multi‐task learning methods mainly have two limitations: (1) They cannot extract enough code‐related information or only extract similar semantic features. Similar multi‐task makes the information in the features increased insufficiently. However, the high difference multi‐task is challenging to converge. (2) They cannot train multi‐task on heterogeneous datasets. In standard multi‐task training, we need to label all tasks for all data, which consumes enormous labor. To solve the above limitations, we select two high difference tasks, the cross‐language code completion task and variable misuse task, to extract expressive semantic code features. We propose an attention‐based feature fusion module to merge information among high difference tasks, avoiding the convergence dilemma of standard multi‐task learning. We propose a federated learning framework, extracting semantic information and using the feature fusion module to integrate multi‐task information among single labeled datasets. We experiment on C# and Python datasets for the code completion and variable misuse tasks. The results show that the performance of fusion features by FedMTFF improved by up to 22.6% and 15.1% compared to single tasks. We use FedMTFF to perform four cross‐language multi‐task features fusion, exceeding the current best baseline by 24.1%.

Given a target binary function, the binary code search retrieves top-K similar functions in the repository, and similar functions represent that they are compiled from the same source codes. Searching binary code is particularly challenging due to large variations of compiler tool-chains and options and CPU architectures, as well as thousands of binary codes. Furthermore, there are some pivotal issues in current binary code search schemes, including inaccurate text-based or token-based analysis, slow graph matching, or complex deep learning processes. In this paper, we present an unsupervised tensor embedding scheme, Codee, to carry out code search efficiently and accurately at the binary function level. First, we use an NLP-based neural network to generate the semantic-aware token embedding. Second, we propose an efficient basic block embedding generation algorithm based on the network representation learning model. We learn both the semantic information of instructions and the control flow structural information to generate the basic block embedding. Then we use all basic block embeddings in a function to obtain a variable-length function feature vector. Third, we build a tensor to generate function embeddings based on the tensor singular value decomposition, which compresses the variable-length vectors into short fixed-length vectors to facilitate efficient search afterward. We further propose a dynamic tensor compression algorithm to incrementally update the function embedding database. Finally, we use the local sensitive hash method to find the top-$K$similar matching functions in the repository. Compared with state-of-the-art cross-optimization-level code search schemes, such as Asm2Vec and DeepBinDiff, our scheme achieves higher average search accuracy, shorter feature vectors, and faster feature generation performance using four datasets, OpenSSL, Coreutils, libgmp and libcurl. Compared with other cross-platform and cross-optimization-level code search schemes, such as Gemini, Safe, the average recall of our method also outperforms others.

With the rapid development and proliferation of multimedia systems and applications, there is a growing need to handle multimedia heterogeneous data safely and efficiently on the cloud. Tensor models are effective in representing multimedia multidimensional data, and the tensor decomposition is one of the basic building blocks of data analysis and learning models. In this article, we propose a secure tensor singular value decomposition (${S}$-tSVD), in which the time-domain operation is converted into a scheme featuring frequency-domain multilinear circular unfolding–folding. First, we represent various multimedia data as cipher subtensors, using fully homomorphic encryption. We then take the fast Fourier transform (FFT) approach to launch a new multiplication operation along the tubal fibers of a unified high-order tensor. Second, relying on the homomorphism of addition and multiplication theory, we prove the fully homomorphic consistency of the proposed${S}$-tSVD algorithm. Third, we provide an elegant solution to tackle the typical dimensionality inconsistency problem while working with multiple subtensors. Finally, we carry out theoretical analyses with respect to dimensionality reduction, reconstruction error of${S}$-tSVD, running time, and data security. We use real unstructured video data and semistructured XML documents, integrating them within a unified tensor model for decomposition. We demonstrate that the error ratio of the${S}$-tSVD is lower than the same compression ratio compared to the SVD decomposition and tSVD-slice approaches. Moreover, the specific${S}$-tSVD decomposition not only enables effective data mining and dimensionality reduction but also ensures the accuracy of the decomposition result and data privacy protection.

People's dependence on search engines allows various computer viruses to spread faster and stronger. Most scholars have neglected the influence of search engines on virus propagation and immunity. It is impossible to immunize all users at the same time with a huge system like social networks. So the main problem is how to pick a fixed-scale node cluster as the source of immunity in the network, which can make other individuals immune and continue to spread (called immune seeds). The immune seeds are scattered on some web pages of search engines to reduce the network virus infection rate. We establish two models, one is the model of computer virus early propagation based on the search engine, and the other is the model of the virus propagation and immunization model. Then we propose an improved immunization strategy: Community Partition immunization strategy based on the target immunization strategy. And we use four real datasets and two simulated datasets to do the simulation experiments, which shows that search engine can promote the propagation of the virus and the immune seeds, and the efficiency of the Community Partition immunization strategy is slightly higher than the target immunization strategy based on degree under the same conditions.

Summary Regarding the current situation that the recognition rate of malware is decreasing, the article points out that the reason for this dilemma is that more and more targeting malware have emerged, which share little or no common feature with traditional malware. The premise of malware recognition judging whether a software is malicious or benign is actually a decision problem. We propose that malware discrimination should resort to the corresponding task or purpose. We first present a formal definition of a task and then provide further classifications of malicious tasks. Based on the decidable theory, we prove that task performed by any software is recursive and determinable. By establishing a mapping from software to task, we prove that software is many‐to‐one reducible to corresponding tasks. Thus, we demonstrate that software, including malware, is also recursive and can be determined by the corresponding tasks. Finally, we present the discrimination process of our method. Nine real malwares are presented, which were firstly discriminated by our method but at that time could not be identified by Kaspersky, McAfee, Symantec Norton, or Kingsoft Antivirus.

Today search engines are tightly coupled with social networks, and present users with a double-edged sword: they are able to acquire information interesting to users but are also capable of spreading viruses introduced by hackers. It is challenging to characterize how a search engine spreads viruses, since the search engine serves as a virtual virus pool and creates propagation paths over the underlying network structure. In this paper, we quantitatively analyze virus propagation effects and the stability of the virus propagation process in the presence of a search engine. First, although social networks have a community structure that impedes virus propagation, we find that a search engine generates a propagation wormhole. Second, we propose an epidemic feedback model and quantitatively analyze propagation effects based on a model employing four metrics: infection density, the propagation wormhole effect, the epidemic threshold, and the basic reproduction number. Third, we verify our analyses on four real-world data sets and two simulated data sets. Moreover, we prove that the proposed model has the property of partial stability. Evaluation results show that, compared the cases without a search engine, virus propagation with the search engine has a higher infection density, shorter network diameter, greater propagation velocity, lower epidemic threshold, and larger basic reproduction number.

The propagation methods of viruses are diverse and studying the virus propagation is a hot topic. There appears a new way that the search engine quickly spreads network viruses, and many researches overlook its impact of propagation and few researches set a model to quantifiabely analyze how the search engine spread viruses. Based on community networks, this paper designs a specific model how the search engine spreads viruses. Moreover, this paper quantifiabely calculate the virus propagation velocity and the propagation effect. First, by analyzing the propagation process of viruses under the search engine condition, we design a positive feedback model to analyze how the search engine and the community network influence the propagation process of viruses. Second, we define relationship functions of propagation factors and calculate the rate of infected nodes while establishing mathematical propagation formulas for two situations. One situation is with the search engine, and another is without the search engine. Third, we design the experiment to verify the model analysis. Compared with two situations, we show that viruses have a much quicker propagation velocity, and the growth rate of infected rate is larger under the search engine condition. When the immune vaccine replaces the virus, this paper is also applicable.

Mobile ad-hoc networks (MANET) has highly dynamic topology, open access of wireless channel and unpredictable behaviors, however, absence of effective security mechanism render MANET more vulnerable to positive attacks. Conventional assessments always require large sample data satisfy specific distribution and establish models through subjective recognition, thus lack common applicability, objectivity and reliability. To solve this problem and make accurate assessment, we propose RAPCA-PP model on basis of Projection Pursuit theory to realize both risk assessment and attributes analysis. Due to Projection Pursuit's theoretical merits, RAPCA-PP is thoroughly data-driven, it can be applied to conditions with small sample quantity, incomplete data and no-prior experience. Using RAGA for solution, RAPCA-PP shows well convergence. Compared with Grey Relations Projection, it demonstrates both better accuracy and higher discrimination. Moreover, our model can analysis attributes by importance and eliminate redundancy. Experiment shows that assessment with eliminated attributes can also correctly reflect each node's performance. RAPCA-PP proved to be suitable for real MANET working scenarios.

P2P Networks are self-organized and distributed. Efficient nodes risk assessment is one of the key factors for high quality resource exchanging. Most assessment methods based on trust or reputation have some remarkable drawbacks. For example, some methods impose too many restrictions to the samples, and many methods can’t identify the malicious recommendations, which result in that the final results are not convincible and credible. To solve these problems, we propose a novel risk assessment method based on grey theory. In our scheme, the communication nodes’ incomplete information state is described as several key attributes. Original data of these attributes is collected using taste concourse method to avoid malicious recommendation. The analysis and computing example shows this scheme is an efficient incomplete information nodes risk assessment method in P2P networks

Mobile ad hoc networks (MANET) is a new networking paradigm for wireless hosts. Because of infrastructureless, self-organization, dynamic topology and openness of wireless links, the routing security problem in MANET is more seriously than in wired networks. Optimized link state routing (OLSR) (T. Clausen et al., 2003) is proposed by IETF's MANET Group at 2003. In OLSR, neighbor detection is not invulnerable when two bad nodes perform wormhole attack. Furthermore, OLSR's security cannot simply rely on IPSec, because OLSR's packets are often broadcasted and IPsec provides end-to-end security. In this paper, we propose a solution to secure OLSR, which apply the wormhole detective mechanism and authentication to strengthen the neighbor relationship establishment, and use hash-chain and digital signature to protect the routing packets.