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Distributed Truss Computation in Dynamic Graphs

Tsinghua Science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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KVM PT Based Coverage Feedback Fuzzing for Network key Devices 2

KVM PT Based Coverage Feedback Fuzzing for Network Key Devic...

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2nd International Conference on Computer engineering and Application, ICCEA 2021

作者： Zhiqiang, Lin Jianshan, Peng Yechuan, Bi Xiaowei, Li State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665426169

With the advent of the network era, network security has attracted more and more attention. As the key node in the network, network key devices play an important role in protecting the internal network and ensuring the network security. For the vulnerability of network key devices, security analysis has become an important concern of security personnel. Fuzzing is an automatic and effective vulnerability mining technology. In this paper, we propose the first coverage feedback fuzzy testing framework based on KVM PT technology for network key devices, aiming to solve the feasibility of applying fuzzy tools in network critical devices. At the same time, a fuzzy test agent technology based on firmware modification is proposed to help speed up the call of testcases. We evaluated the framework on Cisco ASA firewall, and trigger CVE-2018-0101, which proves the effectiveness of the framework. © 2021 IEEE.

关键词： Network security

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Binary Network Protocol Segmentation Based on Probabilistic Inference

SSRN

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SSRN 2023年

作者： Guo, Maohua Fei, Jinlong Zhu, Yuefei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

Protocol Reverse engineering (PRE) is of great practical importance in Internet security-related fields such as intrusion detection, vulnerability mining, and protocol fuzzing. For unknown binary protocols having fixed-length fields, and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance. Hence, this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty. Specifically, we design four related constructions between entropy changes and protocol field segmentation, introduce random variables, and construct joint probability distributions with traffic sample observations. Probabilistic inference is then performed to identify the possible protocol segmentation points. Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results. © 2023, The Authors. All rights reserved.

关键词： Intrusion detection

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An Ensemble Approach with Clustering-Based Under-sampling for Imbalanced Classification 2

An Ensemble Approach with Clustering-Based Under-sampling fo...

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2nd International Conference on Artificial Intelligence and Education, ICAIE 2021

作者： Ren, Huajuan Ren, Shuai Yan, Lin Wang, Ruimin Jing, Jing Shi, Jiaqi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424929

Class imbalance widely occurs in many real-world applications, which affects the recognition of important class to a certain extent. Ensemble methods that combined resampling are effective to alleviate the class imbalance problems. This paper presents a new ensemble approach with clustering-based under-sampling, called CNBoost, for learning from imbalanced data. This algorithm is based on the combination of Centers NN and boosting procedure. Centers NN, as an under-sampling utilizing the nearest neighbors of cluster centers, is used to provide a new training subset in each iteration of boosting, which makes the base learner learn the overall data distribution in each iteration of boosting. We compared the performance of the proposed algorithm with 3 popular ensemble methods. Out of 10 datasets and 3 measurements, CNBoost performs equally well or better than the other 3 methods in 25/30 categories. In addition, we discussed the effect of the base learner used in boosting on the performance of these algorithms. The results show that CNBoost is a promising approach with high classification accuracy and stability for dealing with imbalanced datasets. © 2021 IEEE.

关键词： Iterative methods

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Zero WFuzzer: Target-Oriented Fuzzing for Web Interface of Embedded Devices 4

Zero WFuzzer: Target-Oriented Fuzzing for Web Interface of E...

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4th IEEE International Conference on Computer and Communication engineering Technology, CCET 2021

作者： Zheng, Yan Dong, Weiyu Cheng, Hui State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665438902

With the popularization of embedded devices, security issues continue to occur. The existing automated security testing methods for embedded devices are mainly carried out from two aspects: the Web interface of the device or the firmware binary file. This paper proposes a testing framework ZeroWFuzzer that combines the Web interface of embedded devices with firmware binary, which automatically generates test cases through the Web interface, and uses binary instrumentation to guide the fuzzing test. Experiments in embedded devices have found that ZeroWFuzzer has higher efficiency and quantity of triggering vulnerabilities than the protocol fuzzing tool Boofuzz and the Web security testing tool Xray. © 2021 IEEE.

关键词： Firmware

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Analysis of TLS Encrypted Communication Behavior Based on Bipartite Graph and Traffic Information Embedding 4

Analysis of TLS Encrypted Communication Behavior Based on Bi...

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4th IEEE International Conference on Computer and Communication engineering Technology, CCET 2021

作者： Yan, Lin Jiang, Liehui Wang, Ruimin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665438902

Currently, encrypted traffic using the TLS protocol is widely used, and there are a lot of malicious and unknown behaviors. It is an important goal in the research field to detect the traffic behavior of TLS and maintain network security and stability. This article is based on TLS encrypted traffic, taking the multi-session communication traffic between the client and the server as the object, and carrying out traffic analysis on the generated bipartite graph. Different from the existing works, this paper focuses on the multiple attributes of TLS traffic and transforms it into a bipartite graph with rich attributes, and breaks the bottleneck of single session traffic detection, avoiding simple graph mining mode analysis. The results of anomaly detection and application classification experiments based on real data sets show that similar client application objects and application behaviors show similar traffic characteristics. Finally, we discussed the importance of using the behavioral similarity of client response objects to identify and detect abnormal traffic in network applications. © 2021 IEEE.

关键词： Embeddings

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Mathematical modeling of lunar soft landing - Take chang'e-3 as an example 4

Mathematical modeling of lunar soft landing - Take chang'e-3...

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4th IEEE International Conference on Automation, Electronics and Electrical engineering, AUTEEE 2021

作者： Yang, Benchao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665428392

In this paper, the three-dimensional sphere dynamic model of lunar precise soft landing of the probe is discussed. Taking the National College Students' mathematical modeling competition as the background and Chang'e 3 lunar landing as an example, the lunar center inertial coordinate system and orbital coordinate system are established respectively to better describe the positional relationship between the moon and aircraft and the dynamic changes of aircraft. The coordinates of the near lunar point and the far lunar point are obtained. Through the changes of altitude, speed, pitch angle, yaw angle, longitude and latitude during landing, the landing preparation orbit of Chang'e-3 is determined. Then we analyze the six stages of chang'e-3 landing process one by one, and discuss the results. © 2021 IEEE.

关键词： Students

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Identify IoT Devices through Web Interface Characteristics 6

Identify IoT Devices through Web Interface Characteristics

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6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021

作者： Cheng, Hui Dong, Weiyu Zheng, Yan Lv, Bo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738126043

With the continuous expansion of the business scale of the Internet of Things, the concept of the Internet of Everything has been deeply rooted in the hearts of the people. At the same time, various security issues have been exposed, which has greatly affected the trust of enterprises and users. Discovering and identifying IoT devices as an important part of IoT security should be taken seriously. In this paper, we propose a new method for classifying devices on the Internet of Things, which uses the data characteristics of the main page of the device's web interface to classify the vendor name, even the product name and other information of the device. We randomly selected IP address ranges on the Internet for scanning, and obtained a training set containing 80, 000 entries and covering more than 1, 000 devices from 74 vendors. Using the random forest algorithm and proceeding 10-fold cross-validation, our method classified IoT devices with an accuracy rate of over 97.5%. © 2021 IEEE.

关键词： Internet of Everything

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Optimal scheme for dynamic adjustment of active mirror in FAST system 5

Optimal scheme for dynamic adjustment of active mirror in FA...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhou, Hongchuan Liu, Jiaqi Song, Junyan Yang, Benchao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450384155

This paper mainly studies the optimal scheme of dynamic adjustment of active mirror in FAST system by establishing mathematical model. According to the constraints such as distance variation range, a mathematical model describing the dynamic trajectory of the position and angle of the active mirror is established, and the correctness of the solution of the model is verified by simulation. The optimal surface model is selected from all the results. When the measured object is directly above the fast system, it is modeled based on the ideal parabola to ensure that the expansion constraints and threshold conditions of the actuator are met. Because the distribution of actuator and cable in the whole fast system is not a uniform sphere, the change of radial distance of cable can be considered as the expansion of actuator;In addition, the model is optimized from two aspects: adjusting the position of focus on the horizontal plane and changing the size of zoom alignment. From these two aspects, the parabolic focusing range satisfying the expansion range is obtained. Finally, the optimization strategy of dynamic adjustment of active mirror in fast system is given. © 2021 ACM.

关键词： Actuators

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Integrating KNN and Gradient Boosting Decision Tree for Recommendation 5

Integrating KNN and Gradient Boosting Decision Tree for Reco...

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5th IEEE advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Du, Qiming Li, Nan Yang, Shudan Sun, Daozhu Liu, Wenfu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728180281

As an efficient information filtering tool, the recommendation system can help users obtain valuable information quickly and efficiently while effectively alleviating the information load problem. However, the classic collaborative filtering algorithm has data sparsity problems in practical applications, which affects the accuracy of recommendation. To solve this problem, this paper took movie recommendation as the research object and proposed a hybrid recommendation algorithm named K-GBDT which combined KNN and gradient boosting decision tree. Firstly, it used KNN to obtain the similar information of the target user and item for preliminary prediction. At the same time, it mined users' basic information features and potential features of users and items from the original data. Then, it tried to adopt XGBoost, LightGBM and CatBoost algorithm which were proposed base the idea of gradient boosting decision tree to build regression model. Finally, the target user's rating for the movie was predicted with the trained model, and the recommendation list was generated based on the predicted results. Compared with some classic recommendation algorithms such as NMF, Slope One, Co-Clustering and etc. Based on MovieLens datasets, results show that the mean absolute error and root mean square error of this algorithm are both low, and its recommendation accuracy is higher. © 2021 IEEE.

关键词： Decision trees

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