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Multi-protocol comprehensive topology probe technology under non-cooperative conditions

Multi-protocol comprehensive topology probe technology under...

引用

2022 International Symposium on Computer Applications and Information Systems, ISCAIS 2022

作者： Lu, Xuanting Yang, Qichao Liu, Shengli Yang, Pengfei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781510655010

With the rapid development of the Internet and the increasingly complex and diverse network environment, it is of great significance to quickly and accurately obtain the topology environment of the target network for optimizing network management, locating network resources and ensuring network security. Most of the existing network topology detection methods are implemented based on the SNMP protocol, or use the Traceroute-like method for active detection. However, in a non-cooperative environment, that is, the router disables SNMP permissions or prohibits responding to probe packets to shield Traceroute probes, network probes will face problems such as missing information and blocked probes. Based on the original SNMP protocol detection, this paper adopts RIP, OSPF and other routing protocols to supplement the detection method, which greatly reduces the number of undetectable nodes. In addition, for a small number of router devices that are still undetectable, the Traceroute directional breakthrough method using the routing table as a clue is used to bypass these undetectable nodes, complete the missing information in the network topology, and further optimize and improve the detection results. © 2022 SPIE

关键词： Network topology

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Ensuring kernel integrity using KIPBMFH 1

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作者： Chen, Zhifeng Li, Qingbao Guo, Songhui Wang, Ye State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9783319298146

ISBN: (纸本)9783319298139

Kernel-level malwares are a serious threat to the integrity and security of the operating system. Current kernel integrity measurement methods have one-sidedness in selecting the measurement objects, and the characters of periodic measurement make TOC-TOU attacks unavoidable. The kernel integrity measurement methods based on hardware usually suffer high cost due to the additional hardware, while the kernel integrity measurement methods based on host are always likely to be passed. To address these problems, a kernel integrity protection approach based on memory forensics technique implemented in Hypervisor (KIPBMFH) is proposed in this paper. We first use memory forensics technology to extract the static and dynamic measurement objects, and then adopt time randomization algorithm to weaken TOC-TOU attacks. The experimental results show that KIPBMFH can measure the integrity of the operating system effectively, and has reasonable performance overhead. © Springer International Publishing Switzerland 2016.

关键词： Malware

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Computer evidence analysis technology based on weighted frequent pattern growth algorithm 5th

Computer evidence analysis technology based on weighted freq...

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Xue, Tianxiao Li, Qingbao Zhang, Ping Chen, Zhifeng Feng, Peijun Luo, Nan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030242701

The current analysis of computer forensics is still dependent on the investigation personnel, leading to the problem of heavy workload and low efficiency. At the same time, computer evidences have the characteristics of complex structure and large amount of data, which is prone to the problem of association rules redundancy. In order to improve the efficiency of computer evidence analysis, this paper uses association analysis technology to analyze the user's behavior habits from the obtained computer system log, browser records, file operation traces and other information;combined with the idea of weighted features, the weight set was generated according to the difference of attribute importance, and the Frequent Pattern Growth (FP-Growth) algorithm is improved. The experimental results show that the improved FP-Growth algorithm can effectively remove the redundant rules in evidence analysis, and is more practical for user behavior analysis. © Springer Nature Switzerland AG 2019.

关键词： Efficiency

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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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Research on Performance Optimization for Sparse Matrix-Vector Multiplication in Multi/Many-core Architecture 2

Research on Performance Optimization for Sparse Matrix-Vecto...

引用

2nd International Conference on Information Technology and Computer Application, ITCA 2020

作者： Wang, Qihan Li, Mingliang Pang, Jianming Zhu, Di State Key Laboratory of Mathematical Engineering and Advanced Computing zhengzhou China

ISBN: (纸本)9780738111414

Sparse matrix vector multiplication is an important and commonly used computing kernel in scientific computing. Irregular arrangement of non zeros in sparse matrix leads to irregular memory access pattern, which in turn affects the running speed. In the past ten years, there have been many optimization methods of sparse matrix vector multiplication based on different ideas and techniques. In this paper, the commonly used performance optimization techniques of sparse matrix vector multiplication in multi/many-core architecture are comprehensively investigated. We classify technical methods according to their common characteristics, and discuss the problems encountered by researchers of various methods. In addition, We provide a typical large sparse matrix set for testing. This paper also provides a theoretical basis for our subsequent sparse matrix calculation in Sunway TaihuLight archiecture. © 2020 IEEE.

关键词： Memory architecture

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An Abnormal Domain Name Generation Method Based on a Character-Level Model 4

An Abnormal Domain Name Generation Method Based on a Charact...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Wu, Chunbing Fei, Jinlong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450398343

Internet technology develops by leaps and bounds, new types of botnet attacks are springing up. Currently, most malware are stealthy when implementing attacks since they can bypass detection strategies such as blacklists and sinkholes. These malware mainly use DGA to generate many random domain names to prevent C&C server IP addresses from being traced to the source. To resist botnet attacks, machine-learning methods have been proposed for detecting abnormal domain names. However, there are still some inadequacies, e.g., detection methods based on manual feature extraction couldn't identify novel variant DGA domains, and existing neural network detection methods have weak generalization ability and lack training data for new variant DGA domain names. To address the above problems, this paper proposes an abnormal domain name data generation technology based on a character-level model. Firstly, combining specific text generation-related ideas improves the character-level language model. Secondly, the Bi-GRU network model and attention mechanism are used to thoroughly learn the internal correlation features, and simulate the generation of abnormal domain names. The experimental results show that this method's abnormal domain name data is highly similar to the actual domain name in relative entropy and character composition. Furthermore, it verifies the feasibility of the generated data as training data set for a new variant DGA domain name, which to a certain extent, helps to improve the generalization ability of the abnormal domain name detection model. © 2022 ACM.

关键词： Botnet

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Malware analysis platform based on software gene for cyberspace security practice teaching 2

Malware analysis platform based on software gene for cybersp...

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2nd International Conference on Computer Science and Educational Informatization, CSEI 2020

作者： Liu, Fudong Zhang, Ping Hou, Yifan Wang, Lixin Shan, Zheng Wang, Junchao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728170084

ISBN: (纸本)9781728170077

In recent years, a large number of malware has spread indiscriminately, which causes various significant cyberspace security incidents all over the world. Therefore, malware detection has become one of the important research topics in cyberspace security. At present, however, the practical teaching for malware detection mainly focuses on delivering the theory and skills, and actual combat training is relatively weak. As a novel method for malware recognition and analysis, software gene technology can quickly explore potential functional and behavioral information of malware, and detect unknown malware. Hence, building an experimental platform for malware gene analysis can effectively reduce the time required for students to quickly carry out the actual analysis process, support student needs for comprehensive data analysis, and provide a unified public service infrastructure for classroom teaching, practical training and scientific research. © 2020 IEEE.

关键词： Genes

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Register one-to-one mapping strategy in dynamic binary translation

引用

International Conference on Network Security and Communication engineering, NSCE 2014

作者： Cui, Y.N. Pang, J.M. Shan, Z. Yue, F. State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781138028210

Dynamic Binary Translation (DBT) is an important technology to cross-platform software transplantation such as enhancing the compatibility of Alpha. However, there exist some factors that impede its performance: high translation overhead and translated code of low quality. In this work, we take advantage of host registers, using register mapping strategy to promote the performance of DBT. By mapping registers from guest Instruction-Set Architectures (ISA) to host machines, it could off-load the overhead caused by load and store operations of memory. By simplifying the rules of the intermediate code generated, it could lower the number of intermediate code and improve the quality of translated code. Form X86 to ALPHA on SPEC 2006, the code expansion reduces 32.86% and the performance of the optimized translator is improved by 7.55%. © 2015 Taylor & Francis Group, London.

关键词： Mapping

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Code Obfuscation Based on Inline Split of Control Flow Graph

Code Obfuscation Based on Inline Split of Control Flow Graph

引用

2021 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2021

作者： Li, Yang Sha, Zihan Xiong, Xiaobing Zhao, Yuntian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665418676

Control flow graph is an important concept in the field of program analysis, which represents the source program in the form of graph. On the control flow graph, we can easily analyze the predecessor and successor relationship among basic blocks. This paper we inline the functions in the original program and outline the inlined program randomly to form outline functions. Therefore, we convert the intra-procedure basic block jump to an inter-procedure function call, which hides the basic block jump relationship in the process and obfuscates the function boundaries. In order to further increase the intensity of obfuscation, a bogus function similar to the outline function is added, and the bogus control flow is added in combination with opaque predicates simultaneously. Based on the above work, this paper proposes an obfuscation algorithm which converges and splits the program control flow graph. We utilize the commonly used cryptographic hash algorithm as the test set, and verify the feasibility and effectiveness of the obfuscation algorithm. © 2021 IEEE.

关键词： Graphic methods

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SWPy: Python Numerical computing Library Optimization for Domestic Many-core Processors 10

SWPy: Python Numerical Computing Library Optimization for Do...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Wang, Qihan Pang, Jianming Yue, Feng Yang, Shudan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

The NumPy computational library, the most typical scientific computing library in Python, is generally limited to a single-node or multi-threaded CPU-only execution model. With the increasing size of datasets, the increasing complexity of programs, and their growing computational overhead, these resources are far beyond what a single CPU node can provide, and there is a growing need to solve these problems by utilizing more massive computational resources. In this paper, we port the NumPy numerical computation library for domestic heterogeneous multicore processors and make full use of the huge computational resources of sunway multicore processors to optimize the parallel acceleration of common key functions in the NumPy library, forming the heterogeneous multicore Python computation library - SWPy. © 2022 IEEE.

关键词： Python

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