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Model for Software Behaviour Detection Based on Process Algebra and system Call

China Communications 2013年第11期10卷 24-36页

作者：申利民王涛马川 College of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Normal University of Science and Technology

Behaviour detection models based on automata have been studied widely. By add- ing edge ε, the local automata are combined into global automata to describe and detect soft- ware behaviour. However, these methods in- troduce nondeterminacy, leading to models that are imprecise or inefficient. We present a model of software Behaviour Detection based on Process Algebra and system call （BDPA）. In this model, a system call is mapped into an action, and a function is mapped into a process We construct a process expression for each function to describe its behaviour. Without con- strutting automata or introducing nondeter- minacy, we use algebraic properties and algo- rithms to obtain a global process expression by combining the process expressions derived from each function. Behaviour detection rules and methods based on BDPA are determined by equivalence theory. Experiments demon- strate that the BDPA model has better preci- sion and efficiency than traditional methods.

关键词： intrusion detection software be-haviour model static analysis process algebra system call

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A sketch recognition method based on bi-modal model using cooperative learning paradigm

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Neural Computing and Applications 2024年第23期36卷 14275-14290页

作者： Zhang, Shihui Wang, Lei Cui, Zhiguo Wang, Shi School of Information Science and Engineering Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China School of Information Technology Hebei University of Business and Economics Xuefu Road Hebei Province Shijiazhuang050061 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Street West Section Hebei Province Qinhuangdao066000 China

Static image is an important form of displaying a sketch, representing the appearance information of the sketch. And a stroke sequence composed of several points can also express the shape and contour information of the sketch. Therefore, it is very reasonable to treat a sketch as point-modal data and image-modal data simultaneously. In this paper, a method based on bi-modal model using cooperative learning paradigm is proposed for the sketch recognition task. Specifically, in the point-modal branch, a structural point convolution block is developed by properly dividing local regions to preserve the structural information. In the image-modal branch, the hierarchical residual structure is used to fully extract image-modal features. To reduce the negative impact of noisy samples on the recognition performance, a cooperative learning paradigm is designed based on different perceptual abilities of two modal branches on noisy samples, that is, when training the two branches, the noisy samples can be filtered out through information exchanges and mutual learning. Extensive experiments on the sketch datasets TU-Berlin and QuickDraw show that the proposed method outperforms most baseline methods and has many advantages such as no dependence on additional data and stroke information. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Multi-label feature selection with feature reconstruction and label correlations

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Expert systems with Applications 2025年 285卷

作者： Zhang, Ning Wang, Ao Lu, Pengwei Feng, Tao Xu, Ying Du, Guodong School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Hebei Key Laboratory of Computer Virtual Technology and System Integration Yanshan University Qinhuangdao066004 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Shenzhen Research Institute of Yanshan University Shenzhen518063 China

With the arrival of the big data era, multi-label data have frequently appeared in various fields. However, multi-label data often contain rich feature information, leading to a high-dimensional feature space. Thus, multi-label feature selection plays a crucial role in addressing this challenge as a preprocessing step. Multi-label data assign a set of labels to each instance simultaneously;therefore, capturing the correlations among labels is an effective means of enhancing the learning performance. However, current multi-label feature selection methods seldom consider label correlations. Moreover, redundant features can cause overfitting, increase the complexity, and reduce the interpretability. To address these issues, we propose a multi-label feature selection method that incorporates feature reconstruction and label correlations. Specifically, we construct a low-dimensional embedding from the original feature space using induction, which preserves the manifold structure of the feature space. Next, we explore the global and local label correlations using a manifold regularization term designed by the generated low-dimensional space. We then achieve feature reconstruction by minimising the reconstruction error and sparse regularization terms to learn the relationships between the features. Finally, we construct an optimal objective function with 2,1-norm regularization to facilitate multi-label feature selection and ensure convergence. Comparative experiments on 11 datasets show that the proposed method exhibits notable performance improvements over existing methods(e.g., achieves 5.6 % higher Average precision than RFS on Scene and 3.6 % lower Coverage than CRASG on Science), validating the effectiveness and superiority of the proposed method. © 2025 Elsevier Ltd

关键词： Feature extraction

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Research on Software Community Division Method Based on Inter-node Dependency 16th

Research on Software Community Division Method Based on Inte...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Dong, Jun Hao, Chengqian Ren, Jiadong Lu, Haitao College of Information Science and Engineering Yanshan University Qinhuangdao066000 China The Key Computer Virtual Technology and System Integration Laboratory of Hebei Province Qinhuangdao066000 China

ISBN: (纸本)9783030607982

The mining of software community structure is of great significance in identifying software design pattern, software maintenance, software security and optimizing software structure. To improve the accuracy of description of software execution status model, A new method to construct merge software execute graph is proposed that based on the software execution network model. And then to get the software execution path through the software execution sequence analysis, The ND (Node Dependence) was proposed to measure the tightness of inter-node dependencies based on the design principle of "high cohesion, low coupling" of software. At last, depends on the software execution path, the SLCA (Software Label Communities Algorithm) community division algorithm is proposed to improve stability of community partition algorithm and validity of software module partition. © 2020, Springer Nature Switzerland AG.

关键词： Complex networks

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Research on the Influence of Improved K-shell Algorithm on Commodity Profit 2

Research on the Influence of Improved K-shell Algorithm on C...

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2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Chen, Jing Huang, Jincheng College of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao066004 China

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities. © Published under licence by IOP Publishing Ltd.

关键词： Profitability

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Research on a Novel Influence Maximization Algorithm Based on Community Structure 2

Research on a Novel Influence Maximization Algorithm Based o...

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2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Chen, Jing Liu, Jiangchuan College of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao066004 China

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model. © Published under licence by IOP Publishing Ltd.

关键词： community division community structure independent cascade model influence maximization overlapping influence Seed node

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Learning to Cluster Person in the Video via Graph Convolution Network 13

Learning to Cluster Person in the Video via Graph Convolutio...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Li, Wei Liang, Sixin Bian, Ang Du, Guodong College of Computer Science Sichuan University Chengdu China School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350319156

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module;(ii) a pose-guided embedding module;(iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior. © 2023 IEEE.

关键词： Convolution

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Human Movement Detection and Gait Periodicity Analysis Using Channel State Information 12

Human Movement Detection and Gait Periodicity Analysis Using...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Liu, Zijuan Wang, Lin Liu, Wenyuan Li, Binbin School of Information Science and Engineering Yanshan University China Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province China School of Economics and Management Yanshan University Qinhuangdao HeBei China

ISBN: (纸本)9781509056965

Under the influence of multipath effects and small scale fading, the robustness and reliability of the existing human detection methods based on radio frequency signals are easy to be impaired. In this paper, we propose a novel design that using the multi-layer filtering of channel state information (CSI) to identify moving targets in dynamic environments and analyze the gait periodicity of human. We employ an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose a profile matching mechanism for human detection and a periodicity analysis mechanism for human gait taking advantage of the above design. We evaluated it with the commodity Wi-Fi infrastructures in different environments. Experimental results indicate that our approach performs identification of human with an average accuracy of 94%. © 2016 IEEE.

关键词： Principal component analysis

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Robust visual tracking via combinative deep learning

Robust visual tracking via combinative deep learning

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International Conference on Mechanical, Electric and Industrial Engineering, MEIE 2018

作者： Wen, Jia Zhao, Jiwei Liu, Pengfei Wang, Hongjun Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Information Science and Engineering College Yanshan University Qinhuangdao China State Key Laboratory of Software Engineering of Hebei Province Yanshan University Qinhuangdao China

Object tracking is a hot topic in computer vision. In recent years, a large number of trackers has been proposed, in which the deep learning tracker has achieved excellent performance. The real-time capability of the deep learning tracker is not good enough due to the high-complexity of the network structures. This paper proposed an innovative tracking method to solve this problem. There are three important differences between this tracker and the other deep learning trackers. Firstly, the overcomplete basis in the deep learning tracker results in heavy computational cost. In order to reduce the complexity of the network, fewer units are used in the first hidden layer to replace the overcomplete basis. Secondly, a training method combining two observation models is used in the tracking process. The denoising automatic encoder is used in the first layer and the backpropagation is used in the other layers. This can avoid the diffusion of gradients which is caused by BP and adapt to the change of the targets easier. Thirdly, this tracker using adaptive particle filter to track targets. The number of particles is dynamic changes in tracking process. In this paper, we use different kinds of unlabelled datum to train network and initialize observation model. The observation model uses the samples collected in the tracking to adjust dynamically so as to adapt to the target appearance and complex environment. Compared with the existing methods, the results of experiments in different video sequences show that this tracker has a higher speed and the similar accuracy compared. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Mining important nodes in complex software network based on ripple effects of probability 19

Mining important nodes in complex software network based on ...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Ren, Jiadong Huang, Guoyan Wang, Qian He, Haitao Liu, Xinqian Zhao, Xiaolin Computer Virtual Technology and System Integration Laboratory of Hebei Province College of Information Science and Engineering Yanshan University Qinhuangdao Hebei China Beijing Key Laboratory of Software Security Engineering Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781450371582

The complexity of software directly leads to an increasing cost in software testing and maintenance. Finding the important nodes with significant vulnerability is helpful for fault discovery and further reduces the damage to the software system. In this paper, a new algorithm named MIN-REP (Mining the Important Nodes based on Ripple Effects of Probability) is proposed to find out the paths with greater possibility for fault propagation, and then the important nodes are mined. To build a model of directed unweighted software network, functions are taken as the nodes and the dependencies between the functions are regarded as the edges. Fault propagation tendency paths are discovered based on the function execution paths and minimum probability threshold. The frequency of each directed edge in the set of fault propagation tendency path is taken as the weight of the corresponding edge. Then some metrics related to ripple effects of probability are calculated. Finally, the nodes with the metric at top-k are taken as the important nodes. The experiment verifies the accuracy and efficiency of the algorithm MIN-REP. © 2019 Association for Computing Machinery.

关键词： Probability

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