Biblio

At present, our English error correction algorithm is slightly general, the error correction ability is also very limited, and its accuracy rate is also low, so it is very necessary to improve. This article will further explore the problem of syntax error correction, and the corresponding algorithm model will also be proposed. Based on deep learning technology to improve the error correction rate of English grammar, put forward the corresponding solution, put forward the Sep2seq-based English grammar error correction system model, and carry out a series of rectifications to improve its efficiency and accuracy. The basic architecture of TensorFLOW is used to implement the model, and the success of the error correction algorithm model is proved, which brings great improvement and progress to the success of error correction.

Deep learning models rely on single word features and location features of text to achieve good results in text relation extraction tasks. However, previous studies have failed to make full use of semantic information contained in sentence dependency syntax trees, and data sparseness and noise propagation still affect classification models. The BERT(Bidirectional Encoder Representations from Transformers) pretrained language model provides a better representation of natural language processing tasks. And entity enhancement methods have been proved to be effective in relation extraction tasks. Therefore, this paper proposes a combination of the shortest dependency path and entity-enhanced BERT pre-training language model for model construction to reduce the impact of noise terms on the classification model and obtain more semantically expressive feature representation. The algorithm is tested on SemEval-2010 Task 8 English relation extraction dataset, and the F1 value of the final experiment can reach 0. 881.

Unsupervised cross-domain NER task aims to solve the issues when data in a new domain are fully-unlabeled. It leverages labeled data from source domain to predict entities in unlabeled target domain. Since training models on large domain corpus is time-consuming, in this paper, we consider an alternative way by introducing syntactic dependency structure. Such information is more accessible and can be shared between sentences from different domains. We propose a novel framework with dependency-aware GNN (DGNN) to learn these common structures from source domain and adapt them to target domain, alleviating the data scarcity issue and bridging the domain gap. Experimental results show that our method outperforms state-of-the-art methods.

We introduce the notion of an application-based covert channel—or ABCC—which provides a formal syntax for describing covert channels that tunnel messages through existing protocols. Our syntax captures many recent systems, including DeltaShaper (PETS 2017) and Protozoa (CCS 2020). We also define what it means for an ABCC to be secure against a passive eavesdropper, and prove that suitable abstractions of existing censorship circumvention systems satisfy our security notion. In doing so, we define a number of important non-cryptographic security assumptions that are often made implicitly in prior work. We believe our formalisms may be useful to censorship circumvention developers for reasoning about the security of their systems and the associated security assumptions required.

Vulnerability detection has always been an essential part of maintaining information security, and the existing work can significantly improve the performance of vulnerability detection. However, due to the differences in representation forms and deep learning models, various methods still have some limitations. In order to overcome this defect, We propose a vulnerability detection method VDBWGDL, based on weight graphs and deep learning. Firstly, it accurately locates vulnerability-sensitive keywords and generates variant codes that satisfy vulnerability trigger logic and programmer programming style through code variant methods. Then, the control flow graph is sliced for vulnerable code keywords and program critical statements. The code block is converted into a vector containing rich semantic information and input into the weight map through the deep learning model. According to specific rules, different weights are set for each node. Finally, the similarity is obtained through the similarity comparison algorithm, and the suspected vulnerability is output according to different thresholds. VDBWGDL improves the accuracy and F1 value by 3.98% and 4.85% compared with four state-of-the-art models. The experimental results prove the effectiveness of VDBWGDL.

Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program synthesis. However, most existing methods generate neural program embeddings di-rectly from the program source codes, by learning from features such as tokens, abstract syntax trees, and control flow graphs. This paper takes a fresh look at how to improve program embed-dings by leveraging compiler intermediate representation (IR). We first demonstrate simple yet highly effective methods for enhancing embedding quality by training embedding models alongside source code and LLVM IR generated by default optimization levels (e.g., -02). We then introduce IRGEN, a framework based on genetic algorithms (GA), to identify (near-)optimal sequences of optimization flags that can significantly improve embedding quality. We use IRGEN to find optimal sequences of LLVM optimization flags by performing GA on source code datasets. We then extend a popular code embedding model, CodeCMR, by adding a new objective based on triplet loss to enable a joint learning over source code and LLVM IR. We benchmark the quality of embedding using a rep-resentative downstream application, code clone detection. When CodeCMR was trained with source code and LLVM IRs optimized by findings of IRGEN, the embedding quality was significantly im-proved, outperforming the state-of-the-art model, CodeBERT, which was trained only with source code. Our augmented CodeCMR also outperformed CodeCMR trained over source code and IR optimized with default optimization levels. We investigate the properties of optimization flags that increase embedding quality, demonstrate IRGEN's generalization in boosting other embedding models, and establish IRGEN's use in settings with extremely limited training data. Our research and findings demonstrate that a straightforward addition to modern neural code embedding models can provide a highly effective enhancement.

Pauses in typing are generally considered to indicate cognitive processing and so are of interest in educational contexts. While much prior work has looked at typing behavior of Computer Science students, this paper presents results of a study specifically on the pausing behavior of students in Introductory Computer Programming. We investigate the frequency of pauses of different lengths, what last actions students take before pausing, and whether there is a correlation between pause length and performance in the course. We find evidence that frequency of pauses of all lengths is negatively correlated with performance, and that, while some keystrokes initiate pauses consistently across pause lengths, other keystrokes more commonly initiate short or long pauses. Clustering analysis discovers two groups of students, one that takes relatively fewer mid-to-long pauses and performs better on exams than the other.

Proving secure compilation of partial programs typically requires back-translating an attack against the compiled program to an attack against the source program. To prove back-translation, one can syntactically translate the target attacker to a source one-i.e., syntax-directed back-translation-or show that the interaction traces of the target attacker can also be emitted by source attackers—i.e., trace-directed back-translation. Syntax-directed back-translation is not suitable when the target attacker may use unstructured control flow that the source language cannot directly represent. Trace-directed back-translation works with such syntactic dissimilarity because only the external interactions of the target attacker have to be mimicked in the source, not its internal control flow. Revealing only external interactions is, however, inconvenient when sharing memory via unforgeable pointers, since information about shared pointers stashed in private memory is not present on the trace. This made prior proofs unnecessarily complex, since the generated attacker had to instead stash all reachable pointers. In this work, we introduce more informative data-flow traces, combining the best of syntax- and trace-directed back-translation in a simpler technique that handles both syntactic dissimilarity and memory sharing well, and that is proved correct in Coq. Additionally, we develop a novel turn-taking simulation relation and use it to prove a recomposition lemma, which is key to reusing compiler correctness in such secure compilation proofs. We are the first to mechanize such a recomposition lemma in the presence of memory sharing. We use these two innovations in a secure compilation proof for a code generation compiler pass between a source language with structured control flow and a target language with unstructured control flow, both with safe pointers and components.

This research evaluates the accuracy of two methods of authorship prediction: syntactical analysis and n-gram, and explores its potential usage. The proposed algorithm measures n-gram, and counts adjectives, adverbs, verbs, nouns, punctuation, and sentence length from the training data, and normalizes each metric. The proposed algorithm compares the metrics of training samples to testing samples and predicts authorship based on the correlation they share for each metric. The severity of correlation between the testing and training data produces significant weight in the decision-making process. For example, if analysis of one metric approximates 100% positive correlation, the weight in the decision is assigned a maximum value for that metric. Conversely, a 100% negative correlation receives the minimum value. This new method of authorship validation holds promise for future innovation in fraud protection, the study of historical documents, and maintaining integrity within academia.

A formal modeling language MCD for concurrent systems is proposed, and its syntax, semantics and formal definitions are given. MCD uses modules as basic components, and that the detection rules are not perfect, resulting in packets that do not belong to intrusion attacks being misjudged as attacks, respectively. Then the data detection algorithm based on MCD concurrency model protects hidden computer viruses and security threats, and the efficiency is increased by 7.5% Finally, the computer network security protection system is researched based on security modeling.

In recent years, the need for seamless connectivity has increased across various network platforms with demands coming from industries, home, mobile, transportation and office networks. The 5th generation (5G) network is being deployed to meet such demand of high-speed seamless network device connections. The seamless connectivity 5G provides could be a security threat allowing attacks such as distributed denial of service (DDoS) because attackers might have easy access into the network infrastructure and higher bandwidth to enhance the effects of the attack. The aim of this research is to provide a security solution for 5G technology to DDoS attacks by managing the response to threats posed by DDoS. Deploying a security policy language which is reactive and event-oriented fits into a flexible, efficient, and lightweight security approach. A policy in our language consists of an event whose occurrence triggers a policy rule where one or more actions are taken.

JavaScript is a client-side scripting language that is widely used in web applications. It is dynamic, loosely-typed and prototype-based with first-class functions. The dynamic nature of JavaScript makes it powerful and highly flexible in almost every way. However, this flexibility may result in what is known as code smells. Code smells are characteristics in the source code of a program that usually correspond to a deeper problem. They can lead to a variety of comprehension and maintenance issues and they may impact fault- and change-proneness of the application in the future. We present TAJSlint, an automated code smell detection tool for JavaScript programs that is based on static analysis. TAJSlint includes a set of 14 code smells, 9 of which are collected from various sources and 5 new smells we propose. We conduct an empirical evaluation of TAJSlint on a number of JavaScript projects and show that TAJSlint achieves an overall precision of 98% with a small number of false positives. We also study the prevalence of code smells in these projects.

Phishing is a type of fraud on the Internet in the form of fake web pages that mimic the original web pages to trick users into sending sensitive information to phisher. The statistics presented by APWG and Phistank show that the number of phishing websites from 2015 to 2020 tends to increase continuously. To overcome this problem, several studies have been carried out including detecting phishing web pages using various features of web pages with various methods. Unfortunately, the use of several methods is not really effective because the design and evaluation are only too focused on the achievement of detection accuracy in research, but evaluation does not represent application in the real world. Whereas a security detection device should require effectiveness, good performance, and deployable. In this study the authors evaluated several methods and proposed rules-based applications that can detect phishing more efficiently.

Logic locking is a holistic design-for-trust technique that aims to protect the design intellectual property (IP) from untrustworthy entities throughout the supply chain. Functional and structural analysis-based attacks successfully circumvent state-of-the-art, provably secure logic locking (PSLL) techniques. However, such attacks are not holistic and target specific implementations of PSLL. Automating the detection and subsequent removal of protection logic added by PSLL while accounting for all possible variations is an open research problem. In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less machine learning-based attack on PSLL that can identify any desired protection logic without focusing on a specific syntactic topology. The key is to leverage a well-trained graph neural network (GNN) to identify all the gates in a given locked netlist that belong to the targeted protection logic, without requiring an oracle. This approach fits perfectly with the targeted problem since a circuit is a graph with an inherent structure and the protection logic is a sub-graph of nodes (gates) with specific and common characteristics. GNNs are powerful in capturing the nodes' neighborhood properties, facilitating the detection of the protection logic. To rectify any misclassifications induced by the GNN, we additionally propose a connectivity analysis-based post-processing algorithm to successfully remove the predicted protection logic, thereby retrieving the original design. Our extensive experimental evaluation demonstrates that GNNUnlock is 99.24% - 100% successful in breaking various benchmarks locked using stripped-functionality logic locking [1], tenacious and traceless logic locking [2], and Anti-SAT [3]. Our proposed post-processing enhances the detection accuracy, reaching 100% for all of our tested locked benchmarks. Analysis of the results corroborates that GNNUnlock is powerful enough to break the considered schemes under different parameters, synthesis settings, and technology nodes. The evaluation further shows that GNNUnlock successfully breaks corner cases where even the most advanced state-of-the-art attacks [4], [5] fail. We also open source our attack framework [6].

A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1\_scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.

Identification of source code authorship is vital for attribution. In this work, a machine learning framework is described to identify source code authorship. The framework integrates the features extracted using natural language processing based approaches and abstract syntax tree of the code. We evaluate the methodology on Google Code Jam dataset. We present the performance measures of the logistic regression and deep learning on the dataset.

As a modern power transmission network, smart grid connects abundant terminal devices and plays an important role in our daily life. However, along with its growth are the security threats. Different from the separated environment previously, an adversary nowadays can destroy the power system by attacking its terminal devices. As a result, it's critical to ensure the security and safety of terminal devices. To achieve it, detecting the pre-existing vulnerabilities in the terminal program and enhancing its security, are of great importance and necessity. In this paper, we introduce Cker, a novel vulnerability detection tool for smart grid devices, which generates an program model based on device sources and sets rules to perform model checking. We utilize the static analysis to extract necessary information and build corresponding program models. By further checking the model with pre-defined vulnerability patterns, we achieve security detection and error reporting. The evaluation results demonstrate that our method can effectively detect vulnerabilities in smart devices with an acceptable accuracy and false positive rate. In addition, as Cker is realized by pure python, it can be easily scaled to other platforms.

Nowadays, cyber physical systems are playing an important role in human life in which they provide features that make interactions between human and machine easier. To design and analysis such systems, the main problem is their complexity. In this paper, we propose a description language for cyber physical systems based on stochastic processes. The proposed language is called SPDL (Stochastic Description Process Language). For designing SPDL, two main parts are considered for Cyber Physical Systems (CSP): embedded systems and physical environment. Then these parts are defined as stochastic processes and CPS is defined as a tuple. Syntax and semantics of SPDL are stated based on the proposed definition. Also, the semantics are defined as by set theory. For implementation of SPDL, dependencies between words of a requirements are extracted as a tree data structure. Based on the dependencies, SPDL is used for describing the CPS. Also, a lexical analyzer and a parser based on a defined BNF grammar for SPDL is designed and implemented. Finally, SPDL of CPS is transformed to NuSMV which is a symbolic model checker. The Experimental results show that SPDL is capable of describing cyber physical systems by natural language.

Because of the rise of the Monroe coin, many JavaScript files with embedded malicious code are used to mine cryptocurrency using the computing power of the browser client. This kind of script does not have any obvious behaviors when it is running, so it is difficult for common users to witness them easily. This feature could lead the browser side cryptocurrency mining abused without the user’s permission. Traditional browser security strategies focus on information disclosure and malicious code execution, but not suitable for such scenes. Thus, we present a novel detection method named MineDetector using a machine learning algorithm and static features for automatically detecting browser-side cryptojacking scripts on the websites. MineDetector extracts five static feature groups available from the abstract syntax tree and text of codes and combines them using the machine learning method to build a powerful cryptojacking classifier. In the real experiment, MineDetector achieves the accuracy of 99.41% and the recall of 93.55% and has better performance in time comparing with present dynamic methods. We also made our work user-friendly by developing a browser extension that is click-to-run on the Chrome browser.

At present, most smart contract vulnerability detection use manually-defined patterns, which is time-consuming and far from satisfactory. To address this issue, researchers attempt to deploy deep learning techniques for automatic vulnerability detection in smart contracts. Nevertheless, current work mostly relies on a single code representation such as AST (Abstract Syntax Tree) or code tokens to learn vulnerability characteristics, which might lead to incompleteness of learned semantics information. In addition, the number of available vulnerability datasets is also insufficient. To address these limitations, first, we construct a dataset covering most typical types of smart contract vulnerabilities, which can accurately indicate the specific row number where a vulnerability may exist. Second, for each single code representation, we propose a novel way called AFS (AST Fuse program Slicing) to fuse code characteristic information. AFS can fuse the structured information of AST with program slicing information and detect vulnerabilities by learning new vulnerability characteristic information.

Vulnerabilities in the source code of software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects of complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. In this paper, we propose an evaluation of vulnerability detection performance on source code representations and evaluate how Machine Learning (ML) strategies can improve them. The structure of our experiment consists of 3 Deep Neural Networks (DNNs) in conjunction with five different source code representations; Abstract Syntax Trees (ASTs), Code Gadgets (CGs), Semantics-based Vulnerability Candidates (SeVCs), Lexed Code Representations (LCRs), and Composite Code Representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML-based techniques suffer from poor performance on class imbalance handling when used in conjunction with source code representations for software vulnerability detection.

With the wide application of video information, the protection of video information from illegal access has been widely investigated recently. An efficient selective encryption scheme for high efficiency video coding (HEVC) based on hyperchaotic Lorenz system is proposed. Firstly, the hyperchaotic Lorenz system is discretized and the generated chaotic state values are converted into chaotic pseudorandom sequences for encryption. The important syntax elements in HEVC are then selectively encrypted with the generated stream cipher. The experimental results show that the encrypted video is highly disturbed and the video information cannot be recognized. Through the analysis of objective index results, it is shown that the scheme is both efficient and security.

Being compliant with the GDPR (and data protection regulations in general) is a difficult task, that calls for manifold, computer-based automated support. In this context, several use cases related to the management and the enforcement of privacy policies and consent call for a machine-understandable policy language, equipped with reliable algorithms for compliance checking and explanations. In this paper, we outline a set of requirements for such languages and algorithms, and address such requirements with a framework based on a profile of OWL2 and a set of policy serializations based on popular formats such as ODRL and JSON. Such ``external'' policy syntax is translated into the ``internal'' OWL2 syntax, thereby enabling semantic compliance checking and explanations using specialized OWL2 reasoners. We provide a precise definition of both the OWL2 profile and the external policy language based on JSON.

Renewed focus on spacecraft networking by government and private industry promises to establish interoperable communications infrastructures and enable distributed computing in multi-nodal systems. Planned near-Earth and cislunar missions by NASA and others evidence the start of building this networking vision. Working with space agencies, academia, and industry, NASA has developed a suite of communications protocols and algorithms collectively referred to as Delay-Tolerant Networking (DTN) to support an interoperable space network. Included in the DTN protocol suite is a security protocol - the Bundle Protocol Security Protocol - which provides the kind of delay-tolerant, transport-layer security needed for cislunar and deep-space trusted networking. We present an analysis of the lifecycle of security operations inherent in a space network with a focus on the DTN-enabled space networking paradigm. This analysis defines three security-related roles for spacecraft (Security Sources, verifiers, and acceptors) and associates a series of critical processing events with each of these roles. We then define the set of required and optional actions associated with these security events. Finally, we present a series of best practices associated with policy configurations that are unique to the space-network security problem. Framing space network security policy as a mapping of security actions to security events provides the details necessary for making trusted networks semantically interoperable. Finally, this method is flexible enough to allow for customization even while providing a unifying core set of mandatory security actions.

The network security problem brought by the cloud computing has become an important issue to be dealt with in information construction. Since anomaly detection and attack detection in cloud environment need to find the vulnerability through the reverse analysis of data flow, it is of great significance to carry out the reverse analysis of unknown network protocol in the security application of cloud environment. To solve this problem, an improved mining method on bitstream protocol association rules with unknown type and format is proposed. The method combines the location information of the protocol framework to make the frequent extraction process more concise and accurate. In addition, for the frame separation problem of unknown protocol, we design a hierarchical clustering algorithm based on Jaccard distance and a frame field delimitation method based on the proximity of information entropy between bytes. The experimental results show that this technology can correctly resolve the protocol format and realize the purpose of anomaly detection in cloud computing, and ensure the security of cloud services.