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International Conference on Information science and Technology (ICIST)

作者： Jiangchao Liu Liqian Chen Longming Dong Ji Wang National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

In recent years, many C code static analyzers, with different abilities of bug detection, have appeared and been applied in various domains. There are so many choices that it becomes hard for programmers to know in detail the strengths as well as limitations of all these analyzers and to find the most suitable ones for their code. In this paper, we propose a benchmark for C code static analyzers, named UCBench, to provide quantitative and qualitative measurements for evaluating analyzers. Being different from other benchmarks, UCBench concentrates more on users' requirements rather than the improvements of bug detecting technique itself. The major components of UCBench include test case database, evaluation metrics and harness. We classify test cases into several groups according to their attributes and design various user-centric evaluation metrics. Besides, we develop some harness to automate the evaluation process. Finally, we demonstrate our benchmark suite over four C code static analyzers: Flawfinder, Cppcheck, Uno and Splint.

关键词： Benchmark testing Libraries Measurement computer bugs Databases Standards Monitoring

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Compositional Verification of UML Dynamic Models

Compositional Verification of UML Dynamic Models

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Asia-Pacific Conference on Software Engineering

作者： Wei Dong Ji Wang Zhichang Qi Ni Rong School of Computer National University of Defense Technology China National Laboratory for Parallel and Distributed Processing China

UML dynamic models are important for software analysis and design. Verifying UML dynamic models to find design errors earlier is a key issue for ensuring software quality. Because of the characteristics such as concurrency and hierarchy, model checking of UML Statecharts and collaboration diagrams faces the problem of state explosion. In this paper, UML Statecharts is firstly structurally expressed by hierarchical automata and its semantics for open systems is introduced. Then, the synchronization composition of objects in UML collaboration diagrams is expatiated, based on which the global system behaviors can be constructed. Based on hierarchical automata and simulation relation between semantics structures, the compositional rules for verifying concurrent object systems are proposed. It makes possible that the construction of global state space will be unnecessary in model checking of UML collaboration diagrams. The hierarchical structures of UML Statecharts are also brought into the compositional verification, which makes the model checking of implementation models can be carried out through replacing detailed components by abstract specifications.

关键词： Unified modeling language Concurrent computing Automata State-space methods Explosions International collaboration Software systems Software engineering distributed computing Laboratories

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DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams

DPSS: Dynamic Parameter Selection for Outlier Detection on D...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Ruyi Zhang Yijie Wang Haifang Zhou Bin Li Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Outlier detection on data streams identifies unusual states to sense and alarm potential risks and faults of the target systems in both the cyber and physical world. As different parameter settings of machine learning algorithms can result in dramatically different performance, automatic parameter selection is also of great importance in deploying outlier detection algorithms in data streams. However, current canonical parameter selection methods suffer from two key challenges: (i) Data streams generally evolve over time, but these existing methods use a fixed training set, which fails to handle this evolving environment and often results in suboptimal parameter recommendations; (ii) The stream is infinite, and thus any parameter selection method taking the entire stream as input is infeasible. In light of these limitations, this paper introduces a Dynamic Parameter Selection method for outlier detection on data Streams (DPSS for short). DPSS uses Gaussian process regression to model the relationship between parameters and detecting performance and uses Bayesian optimization to explore the optimal parameter setting. For each new subsequence, DPSS updates the recommended parameter setting to suit the evolving characteristics. Besides, DPSS only uses historical calculations to guide the parameter setting sampling and adjust the Gaussian process regression results. DPSS can be employed as an auxiliary plug-in tool to improve the detection performance of outlier detection methods. Extensive experiments show that our method can significantly improve the F-score of outlier detectors in data streams compared to its counterparts and obtains more superior parameter selection performance than other state-of the-art parameter selection approaches. DPSS also achieves better time and memory efficiency compared to competitors.

关键词： Training Fault diagnosis Machine learning algorithms Memory management Gaussian processes Detectors Bayes methods

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Entropy and Autoencoder-Based Outlier Detection in Mixed-Type Network Traffic Data

Entropy and Autoencoder-Based Outlier Detection in Mixed-Typ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhongyang Wang Yijie Wang Zhenyu Huang Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data containing categorical and numerical features are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, the existing general algorithms cannot combine the characteristics of outliers in specific fields, leading to an unsatisfying performance in actual scenarios, such as the field of network security. This paper proposes a novel Entropy and Autoencoder-based Outlier Detection in mixed-type network traffic data, termed EAOD, which combines characteristics of outliers in specific fields and machine learning models to detect outliers. EAOD utilizes the expert rules made by domain knowledge summarized based on characteristics of existing outliers to label known outlier data. It performs holoentropy and deep autoencoder for the category and numerical feature spaces, respectively, in unlabeled data to obtain outlier scores integrated to get the final outlier scores via a dynamic integration strategy. Especially in the numerical feature space, to fully mine known outlier behavior patterns, deep autoencoders of outlier and normal types are constructed separately to capture unknown outliers jointly. Experiments show that EAOD significantly outperforms eight state-of-the-art outlier detectors on seven real network traffic datasets.

关键词： Telecommunication traffic Machine learning Detectors Network security Feature extraction Entropy Data models

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Precise pointer analysis for list-manipulating programs based on quantitative separation logic

Precise pointer analysis for list-manipulating programs base...

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International Conference on Information science and Technology (ICIST)

作者： Longming Dong Liqian Chen Jiangchao Liu Renjian Li National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

Full precise pointer analysis has been a challenging problem, especially when dealing with dynamically-allocated memory. Separation logic can describe pointer alias formally, but cannot describe the quantitative reachability between pointers. In this paper, we present a symbolic framework for analyzing the reachability between pointers in list-manipulating programs. The precise points-to relations of pointers in lists are described by formulae of quantitative separation logic (QSL), and the analysis framework is based on the operational and rearrangement rules about the assignments of pointers. The fixpoint calculus and the counter symbolic abstraction are used to find loop invariants. We can get precise relations between pointers at each point of list-manipulating programs. In the end, several initial examples about list-manipulating programs are given to show that the approach can get precise pointer analysis for list-manipulating programs.

关键词： Radiation detectors Shape Instruments Calculus Semantics Data structures Argon

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TRUSTIE: Design of a Trustworthy Software Production Environment

TRUSTIE: Design of a Trustworthy Software Production Environ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huaimin Wang National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

Internet fundamentally changes the model of software development, the demands of software quality, and the process of software resource sharing. Internet- based environment for trustworthy software production is recognized as a key topic of software engineering in both academic and software industry. In this paper, the concepts and models of trustworthy software are introduced which dominate the design of Trustie environment. Trustie provides trustworthy software components sharing by an evolving software repository, and provides collaborative software development in a customizable development platform powered by a software production line framework. Finally the layered practices of research and application based on Trustie preliminarily demonstrate the effectiveness as well as the promising future of this environment.

关键词： Software Production Collaboration Internet Resource management Software engineering Programming

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Image Annotation by Object Hypotheses-oriented Deep Neural Networks

Image Annotation by Object Hypotheses-oriented Deep Neural N...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Fang MA Shao-he LV Ke-xin ZHENG Chi JIN Fei CHEN Ke YANG and Yong DOU National Laboratory for Parallel and Distributed Processing National University of Defense Technology University of South China School of Computer Science and Technology

Image annotation generates a set of semantic labels that describe the contents of an input *** deep learning techniques have achieved significant success in many areas of image *** this paper,we present a multi-label image annotation method that combines unsupervised object hypotheses generation and deep neural *** an image,object hypotheses are generated in an unsupervised *** we extract the image features for each hypothesis with a deep neural network *** combining the features of all hypotheses,we get the features of the entire ***,we calculate for each label the probability of that the label is correlated with the given *** can be trained in an end-to-end way using the standard backward propagation *** results on multiple benchmark datasets show that our method is better than the state-of-the-art ones.

关键词： Deep learning Multi-label annotation Object hypotheses

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Attention-based Encoder-Decoder Recurrent Neural Networks for HTTP Payload Anomaly Detection

Attention-based Encoder-Decoder Recurrent Neural Networks fo...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Shang Wu Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Attack payloads are often short segments hidden in HTTP requests; thus they can be found by HTTP payload anomaly detection. Deep learning models learn data features during training without manual feature extraction, and better performance has received more attention. Recurrent Neural Network models process sequences directly, which are widely used in payload anomaly detection. However, due to the gradient vanishing problem, RNN has limits on processing the long sequences. Meanwhile, RNN uses its final hidden state for detection and pays more attention to the content of the end of the payload. Besides, deep learning generally lacks interpretability. The paper proposes an unsupervised deep learning model for HTTP payload Anomaly Detection, namely Attention-based Encoder-Decoder Recurrent Neural Networks Anomaly Detection model (AEDRAD). AEDRAD utilizes the encoder-decoder RNN and attention mechanism to detect anomalies by reconstructing the original sequences. AEDRAD filters the fields of HTTP protocol that cannot exist anomalies, focusing on the suspicious segments. Through the encoder-decoder network, the normal payload can be well-reconstructed while the anomaly payload fails. With the attention mechanism, AEDRAD generates practical features for further anomaly detection from a global perspective. Meanwhile, it marks abnormal fragments visually, which is conducive to a subsequent analysis by experts. The experimental results show that AEDRAD significantly outperforms three state-of-the-art unsupervised algorithms on two real datasets.

关键词： Deep learning Training Recurrent neural networks Protocols Focusing Manuals Feature extraction

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Are filter methods very effective in gene selection of microarray data?

Are filter methods very effective in gene selection of micro...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Zhou-Jun Li Li-Juan Zhang Huo-Wang Chen School of Computer Science & Engineering Beihang University Beijing China National Laboratory for Parallel and Distributed Processing Changsha China

Feature (gene) selection is a frequently used preprocessing technology for successful cancer classification task in microarray gene expression data analysis. Widely used gene selection approaches are mainly focused on the filter methods. Filter methods are usually considered to be very effective and efficient for high-dimensional data. This paper reviews the existing filter methods, and shows the performance of the representative algorithms on microarray data by extensive experimental study. Surprisingly, the experimental results show that filter methods are not very effective on microarray data. We analyze the cause of the result and provide the basic ideas for potential solutions.

关键词： Filters Data analysis Classification algorithms Cancer Algorithm design and analysis computer science Data engineering Laboratories distributed processing Gene expression

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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