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Journal of Computational Information systems 2015年第2期11卷 535-545页

作者： Ren, Jiadong Li, Weina Han, Gaowei College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Dissimilar path detection is an important task in software behavior detection. Previous dissimilar path mining algorithms ignore time-interval weight and dissimilarity. Therefore, a dissimilar path detecting algorithm called Graph-Dissimilar-Detect in complex software behavior network based on time-interval weight and dissimilarity is put forward. Firstly, weighted complex software behavior network model is built. DFS is adopted for the network to obtain paths. Secondly, Path-Length-Constraint is designed to prune some useless paths. Time-interval-Constraint is devised to prune some weak relation between software behaviors. Strong-Path-Checking is developed to prune weighted key sub-paths. Thirdly, KPath-Mine is designed to mine weighted key paths. Fourthly, definitions of dissimilar paths and path dissimilarity are provided and Dissimilar-Path-Mine is devised to find dissimilar paths. Experimental results show Graph-Dissimilar-Detect algorithm has a good scalability in detecting dissimilar paths in complex software behavior network. Furthermore, considering time-interval and path dissimilarity, more interesting and accurate key paths and dissimilar paths can be found. 1553-9105/Copyright © 2015 Binary Information Press

关键词： Graph theory

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Analysis on impact of node failure in software execution network

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Journal of Computational Information systems 2015年第6期11卷 2217-2225页

作者： He, Haitao Ren, Rong Zhang, Bing Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

One or few failure nodes will cause cascading failure in software network, which would bring about security issues. Thus, analyzing nodes becomes an indispensable aid in software security. In this paper, software execution process is abstracted as weighted complex network. Based on this, a cascading failure diffusion analyzing (CFDA) algorithm is put forward to evaluate the impact of node failure on software during its runtime dynamically. It explores nodes' failure spreading range and the number of infected nodes by malicious attacks on high in-strength nodes and strong failure propagation probability nodes in software execution networks respectively. Meanwhile, the propagation behavior of cascading failures in the software system is studied. The experiment results prove that strong failure propagation ability nodes under attack have greater devastating impact on software than others. Copyright © 2015 Binary Information Press.

关键词： Complex networks

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Grid resource dynamic replication strategy based on access frequency and time cost

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Journal of Computational Information systems 2015年第6期11卷 1973-1982页

作者： Zhang, Zhongping He, Long The School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Data grid provides a large amount of computing resources and storage space to handle data-intensive applications. Because of the limitation of available resources and the production of large-scale workload in grid, the effective use of grid resources has become a major challenge. Data replication can reduce the bandwidth consumption, improve the fault-tolerant ability and enhance the extensibility. A grid resources dynamic replication strategy based on the access frequency and time cost is given in this paper. The proposed strategy deletes the file by considering two important parameters: access frequency and transmission costs. In the replica selection strategy, the best replica is selected by considering the waiting time of the replica request queue in the storage and the data transfer time. The simulated results with OptorSim show that the proposed replication strategy gives better performance compared with DHRA algorithm. Copyright © 2015 Binary Information Press.

关键词： Data transfer

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Mining maximal frequent patterns based on hierarchical bit vector and stack principle

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Journal of Computational Information systems 2015年第1期11卷 369-376页

作者： Wang, Qian Ren, Jiadong He, Haitao Huang, Guoyan College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Maximal frequent pattern mining usually adopts a global pattern growth way, and a maximal frequent pattern can be obtained unless most of its subsets are checked to be frequent. An algorithm for mining maximal frequent patterns based on hierarchical bit vector and stack principle, called MaxPat HB, is proposed in this paper. Different with the traditional thinking of "test after connection", MaxPat HB adopts an individual pattern growth way and uses another thought of "testing while growing" which can efficiently gain the specified maximal frequent pattern and decrease the scale of candidate generation. The original data is converted to hierarchical bit vectors and maximal frequent patterns are mined hierarchically according to item unit. Candidate itemset generation adopts the stack principle, the push and pop operation is imitated by transforming bit values in a vector. Logic calculations between bit vectors are faster than the matches between strings, and the hierarchical structure can extremely reduce the searching space. Experiment result shows its high efficiency and good scalability. ©, 2014, Journal of Computational Information systems. All right reserved.

关键词： Vectors

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Research on Router Algorithm Based on Trust Primary Node in the Environment of P2P Network

Research on Router Algorithm Based on Trust Primary Node in ...

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2015 International Conference on Informatics, Control and Automation(ICA 2015)

作者： Jing CHEN Yun WAN Department of Computer College of Information Science and EngineeringYanshan University Key Laboratory of Computer Virtual Technology and System Integration of Hebei Province

The distributed, heterogeneous and anonymous attribute of P2 P networks make the node information sharing facing security problems. In order to prevent malicious and false service of nodes, and improve the retrieval efficiency, the primary node selection algorithm based on trust mechanism is proposed in this paper. Firstly, the network topology based on virtual community is described, and the function of primary node is discussed. Secondly, the primary node is selected according to direct trust value and recommendation trust value of each node in the virtual community. Finally, resource retrieval algorithm is designed according to the selected trust primary node, and the algorithm is experimentally verified. In some certain extent, the results show that the method is able to reduce the fraud of primary node and improve the retrieval efficiency.

关键词： P2P network virtual community Primary node Trust mechanism Router algorithm

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ENHANCING TWITTER BOT DETECTION VIA MULTIMODAL INVARIANT REPRESENTATIONS

arXiv

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arXiv 2024年

作者： Gong, Jibing Peng, Jiquan Qu, Jin Du, ShuYing Wang, Kaiyu School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China

Detecting Twitter Bots is crucial for maintaining the integrity of online discourse, safeguarding democratic processes, and preventing the spread of malicious propaganda. However, advanced Twitter Bots today often employ sophisticated feature manipulation and account farming techniques to blend seamlessly with genuine user interactions, posing significant challenges to existing detection models. In response to these challenges, this paper proposes a novel Twitter Bot Detection framework called BotSAI. This framework enhances the consistency of multimodal user features, accurately characterizing various modalities to distinguish between real users and bots. Specifically, the architecture integrates information from users, textual content, and heterogeneous network topologies, leveraging customized encoders to obtain comprehensive user feature representations. The heterogeneous network encoder efficiently aggregates information from neighboring nodes through oversampling techniques and local relationship transformers. Subsequently, a multi-channel representation mechanism maps user representations into invariant and specific subspaces, enhancing the feature vectors. Finally, a self-attention mechanism is introduced to integrate and refine the enhanced user representations, enabling efficient information interaction. Extensive experiments demonstrate that BotSAI outperforms existing state-of-the-art methods on two major Twitter Bot Detection benchmarks, exhibiting superior performance. Additionally, systematic experiments reveal the impact of different social relationships on detection accuracy, providing novel insights for the identification of social bots. © 2024, CC BY.

关键词： Tweets

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An algorithm based on compressed tree and bit vector table for mining frequent patterns on uncertain data streams

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Journal of Convergence Information technology 2012年第20期7卷 389-398页

作者： Dong, Jun Zhang, Yuanyuan Song, Wei Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

Most of algorithms based on tree structure for mining frequent pattern on uncertain data streams always store a large number of tree nodes, and record the corresponding information of data streams which can cause massive information storages. In this paper, an algorithm CTBVT based on compressed tree and bit vector table for mining frequent patterns on uncertain data streams, is proposed. The uncertain data streams are initialized to probability-vector table, in the table, the items are represented by transactions, unlike other bit vector tables the occurrence probabilities of items are stored in it. When the window slides, all the columns in probability-vector table are left shift m bits at the same time and m is the number of transactions in the window. We also propose compressed tree in which the items with different probabilities are stored in the same tree nodes, which will reduce the number of tree nodes significantly, then the items and its probability in the tree node correspond to the bit vector table are converted into binary bit vector, the number of 1s in the binary bit vector is the frequency of the tree node. Afterwards, each leaf node of the tree is connected to an array which is used to store the combination of all items and their expected support in the path. The leaf nodes are stored in the LeafList. Finally, we scan the arrays that are linked to the leaf nodes in the LeafList and compare the expected support that is stored in the array with a minimum support threshold minSup to get all the frequent itemsets, mining time will reduce dramatically. Experiment results show that CTBVT is very efficiency and scalable.

关键词： Probability

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A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

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China Communications 2014年第9期11卷 112-123页

作者： YI Huawei ZHANG Fuzhi LAN Jie School of Information Science and Engineering Yanshan University Qinhuangdao 066004 Hebei Province E R.China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 P. R. China Liaoning University of Technology Jinzhou 121001 Liaoning Province P. R. China

The existing collaborative recommendation algorithms have lower robustness against shilling *** this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey ***,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor *** influence of attack profiles on the recommendation results is reduced through adjusting similarities among ***,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature ***,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization *** results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

关键词： shilling attacks robust collaborative recommendation matrix factori-zation k-distance Tukey M-estimator

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A density grid-based uncertain data stream clustering algorithm

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Journal of Computational Information systems 2014年第9期10卷 3619-3626页

作者： He, Haitao Zhao, Jintian The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China

The existing grid-based uncertain data stream clustering algorithms are fast but low-accuracy, and sensitive to user-specified threshold. In order to solve the above problems, a density grid-based uncertain data stream clustering algorithm UG-Stream is proposed in this paper. In UG-Stream algorithm, a dynamic threshold is defined by taking uncertainty and grid feature into account, dense grid can be distinguished by the threshold. The probability variance is defined to describe the distribution of internal data points in grid. If grid distribution can be taken as uniform, dense grid can be classified as core dense grid by probability variance. Core dense grid can be clustered directly, the rest of dense grids will be merged into current existing clusters by probability center distance. Contrast experiments show that UG-Stream algorithm is superior to UMicro algorithm in both clustering accuracy and clustering rate. © 2014 Binary Information Press.

关键词： Clustering algorithms

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virtual grid-based clustering of uncertain data on vulnerability database

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Journal of Convergence Information technology 2012年第20期7卷 429-438页

作者： Dong, Jun Cao, Mengmeng Huang, Guoyan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

Most existing vulnerability taxonomy classifies vulnerabilities by their idiosyncrasies, weaknesses, flaws and faults et al. The disadvantage of the taxonomy is that the classification standard is not unified and there is overlap classification phenomenon in vulnerability taxonomy. In order to solve the problem, we will propose an algorithm VUNClique, virtual Grid-based Clustering of Uncertain Data on vulnerability database. Firstly, this paper transforms the vulnerability database into uncertain dataset using the existing vulnerability database pretreatment model. Secondly, we define a virtual grid structure, the cells are divided into real cells and virtual cells, but only the real cells which contain data objects stored in memory. The probability attribute value similarity is defined to deal with the similarity of non-numeric attributes, which compares the number of non-numeric attributes with the same value between tuples to measure the similarity. We provide a secondary partition algorithm to improve the similarity between the tuples in the same cell, the algorithm merges a tuple into it's high-density neighbor cell which has the maximum value of probability attribute value similarity with it. Then, a novel identify cluster algorithm is provided to cluster the high-density real cells. It can identify clusters of arbitrary shapes by traversing real cells twice. Finally, performance experiments over the uncertain dataset transformed by NVD vulnerability database. The experiments results show that VUNClique can find clusters of arbitrary shapes, and greatly improve the efficiency of clustering.

关键词： Cells

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