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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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Identify IoT Devices through Web Interface Characteristics 6

Identify IoT Devices through Web Interface Characteristics

引用

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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Integrating KNN and Gradient Boosting Decision Tree for Recommendation 5

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

引用

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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DSA-NIE: Double Static Attributes Based Node Importance Evaluation of Inter-Domain Routing System 5

DSA-NIE: Double Static Attributes Based Node Importance Eval...

引用

5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Zhu, Huihu Qiu, Han Zhu, Junhu Wang, Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728147437

Assessing the importance of AS nodes is important for the protection of inter-domain routing system security. However, the existing literature on node importance evaluation methods based on static attributes are difficult to accurately characterize nodes. And which based on dynamic attributes have high time complexity. To address these problems, we analyze the relationship between static structural attributes and dynamic attributes after node failure. It is found that the degree can indirectly reflect the impact of the UPDATE packet propagation and the number of routing paths can reflect the impact caused by load redistribution. And then, we build static attribute characterization model of node failure effect. After that an evaluation method DSA-NIE is put forward. Given the two static attributes, DSA-NIE could effectively assessment of the importance of nodes. When compared with SD-KNI algorithm, the time complexity has been reduced to O(lmn). And the experiment result shows that the failure effect of the key nodes assessed by the DSA-NIE is 7.4% higher than the average, and up to 12.9% higher. © 2019 IEEE.

关键词： evaluation Inter-domain Routing System static attribute

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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...

引用

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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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

引用

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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The Analysis of HTTPS Privacy Protection Based on Malicious Code Injection 20

The Analysis of HTTPS Privacy Protection Based on Malicious ...

引用

20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Liu, Zeyan Pen, Jianshan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou China

ISBN: (纸本)9781728181417

HTTPS protocol is utilized to protect personal privacy in network communication by man-in-the-middle attacks. However, after the browser is attacked, this protection mechanism will be destroyed. This paper analyzes the vulnerability of https protocol under two scenarios of operating system and network data transmission. Based on the untrusted situation, the possibility of implementing HTTPS protocol injection and page injection on the browser side was analyzed. A traffic monitoring and detection scheme based on dynamic rules is proposed, which can effectively detect whether the browser is injected with malicious code. False positive is less than 10%. © 2020 IEEE.

关键词： Network coding

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A Survey of Available Information Recovery of Binary Programs Based on Machine Learning 5

A Survey of Available Information Recovery of Binary Program...

引用

5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Shao, Wenqiang Yang, Qichao Guo, Xixi Cai, Ruijie State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665499132

A binary program is an executable file obtained by compiling the source code. In order to reduce the size of software or prevent reverse engineering, software publishers usually only release executable files that have stripped a lot of source information. The rich source-level information is very helpful for software hardening and malware analysis. In recent years, the rapid development of artificial intelligence has promoted the research of binary program information recovery. This paper summarizes the relevant research on effective information recovery of binary programs since 2016. Firstly, the importance of recovering source-level information from binary programs is introduced. Next, the problems are divided into three categories, compiler traceability, signature information recovery and variable information recovery of source program. Finally, based on the latest technology in this field, the future development direction is analyzed. © 2022 IEEE.

关键词： Machine learning

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