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检索条件"机构=State Key-Laboratory of Mathematical Engineering and Advanced Computing"

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RLGBG: A Reachable Landmark Grouping Based IP Geolocation Method for Unreachable Target 29th

RLGBG: A Reachable Landmark Grouping Based IP Geolocation Me...

引用

29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zu, Shuodi Liu, Chong Zhang, Fan Ding, Shichang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783031429866

IP geolocation technology can estimate or determine the geographical location of terminal device through IP address, which is an important basis for location-based network services. The existing IP geolocation methods based on network measurement cannot accurately obtain the network environment when the target does not respond to the detection message, so it is difficult to achieve high-precision geolocation. To solve this problem, a reachable landmark grouping based IP geolocation method for unreachable target (RLGBG) is proposed in this manuscript. RLGBG first detects landmark data extensively, filters, evaluates and retains reliable and accessible landmarks. Secondly, RLGBG counts the topological relationship and geographical distribution among the reachable landmarks, groups the landmarks according to the common router, and divides the landmarks into different groups after checking by geographical location. Then, the continuous IP segments are divided into IP blocks according to landmark groups, and the location range of each IP block is calculated by the landmarks within each group. Finally, RLGBG counts the IP blocks containing the target, merges those IP blocks that are geographically coincident and topologically related, and estimates the target’s location by calculating the landmark center within the IP blocks. The geolocation experiments based on four cities in China show that RLGBG can effectively estimate the location of unreachable IP targets, and its accuracy and coverage are higher than the existing typical method and IP location databases. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Quantum computing control system configuration optimizer with adaptive genetic algorithm 9

Quantum computing control system configuration optimizer wit...

引用

9th International Symposium on Advances in Electrical, Electronics, and Computer engineering, ISAEECE 2024

作者： Huang, Shucheng Wang, Junchao Pang, Jianmin Shan, Zheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781510683303

As quantum computing continues to advance, the complexity of control systems for superconducting qubits also increases. This paper presents a genetic algorithm (GA) approach for optimizing the control systems in quantum computing. Focusing on minimizing costs, maximizing resource utilization, and reducing device count, the study demonstrates the algorithm's effectiveness in configuring control systems efficiently. Experimental results from five quantum chips, ranging from 64 to 1024 bits, demonstrate significant improvements in cost efficiency and device management after 500 generations of optimization. The findings highlight the algorithm's adaptability to different quantum system scales and its potential in enhancing the scalability and efficiency of quantum computing architectures. © 2024 SPIE.

关键词： Quantum computers

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TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code 5

TS-GGNN: Combining Graph and Sequence Features for Vulnerabi...

引用

5th International Conference on Communications, Information System and Computer engineering, CISCE 2023

作者： Zhou, Xin Pang, Jianmin Zhang, Chunyan Yue, Feng Wang, Junchao Liu, Guangming State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350326796

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities. © 2023 IEEE.

关键词： Network coding

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Research on Unpredetermined Behavior Recognition Techniques Based on Network Attacks 5

Research on Unpredetermined Behavior Recognition Techniques ...

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5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Xiao, Yuxuan Fei, Jinlong Wang, Junyi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798400716485

With the increasing number of cyber-attack behaviors, it has caused great harm to the contemporary society. Currently, the cyber attack behavior recognition module is usually implemented by adopting a strategy based on rule-base matching, which often fails to identify the attack behaviors when faced with unpreset path attack behaviors of the actual attack process due to its reliance on predefined abnormal behavior patterns and attack labels. Therefore, this paper proposes an efficient and accurate unpreset behavior detection framework for cyber-attack behaviors, introduces machine learning techniques into the identification of unpreset attack behaviors, proposes an RF-Corr feature importance assessment method based on the Kendall correlation coefficient between features and the feature weight values, and designs an unpreset attack behavior identification method based on BiLSTM neural network, which improves the current effectiveness of identification and detection for unpresetted behavior of cyber attacks, and provides a solution for artificial intelligence detection of unpresetted behavior of cyber attacks. © 2023 ACM.

关键词： Crime

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Critical Variable Guided Mutation for Directed Greybox Fuzzing

Critical Variable Guided Mutation for Directed Greybox Fuzzi...

引用

2023 IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： He, Yubo Liu, Long The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350399028

Directed greybox fuzzing directs fuzzers to specified code areas and has gained great achievements in 1-day vulnerability detection. However, existing directed graybox fuzzers fail to generate the crash sample even if they found a testcase reaching the target site. There are mainly two questions that affect the effectiveness of directed greybox fuzzing: basic block-level target is coarse enough for 1-day vulnerability detection and the fuzzers follow a specific rule to select operators regardless of the vulnerability itself. This paper points out that only a few vulnerability-related variables are related to the vulnerability triggering. Based on the vulnerability-related variables, this paper proposes critical variable guided mutation, a mutation scheduling method to enhance the crash reproduction capability of directed greybox fuzzing. We implemented a prototype MDGF based on the critical variable guided mutation and evaluate it on real world programs. Evaluation of MDGF on various real-world programs showed that MDGF found vulnerabilities faster than the mainstream directed greybox fuzzers. The experimental results showed that the speed of MDGF is 6.18 times faster than that of AFLGo and 1.40 times faster than Beacon, and MDGF can find 1.71x more bugs than AFLGo. © 2023 IEEE.

关键词： Cell proliferation

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An Improved key Mismatch Attack on Kyber 7

An Improved Key Mismatch Attack on Kyber

引用

7th International Conference on Cryptography, Security and Privacy, CSP 2023

作者： Wang, Yaru Jiang, Haodong Ma, Zhi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350323368

Recently, American National Institute of Standards and Technology (NIST) announced Kyber as the first KEM candidate to be standardized. The security of Kyber is based on the modular learning with errors (MLWE) problem, which achieves excellent efficiency and size. This work proposes an improved key mismatch on Kyber, which can reduce the number of queries required to recover the secret key. We first transform the problem of finding a certain parameter of ciphertexts into a quantum ordered search problem. Then we give the procedure of finding the value of a parameter in the ciphertexts by the quantum method. Finally, we instantiate this attack method on Kyber512, Kyber768 and Kyber1024. Compared with the existing attack algorithm, our improved attack reduces the number of queries for Kyber512, Kyber768 and Kyber1024 by 63%, 59% and 45%, respectively. © 2023 IEEE.

关键词： Security of data

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Dynamic Resampling Based Boosting Random Forest for Network Anomaly Traffic Detection 36th

Dynamic Resampling Based Boosting Random Forest for Network...

引用

36th International Conference on Industrial, engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023

作者： Ren, Huajuan Wang, Ruimin Dong, Weiyu Li, Junhao Tang, Yonghe State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783031368219

Network anomaly traffic detection is an important technique for detecting intrusion activities and maintaining cyberspace security. Random forest is widely used in network anomalous traffic detection due to its good detection performance. However, Random Forest suffers from the insufficient ability to handle difficult samples and poor performance in dealing with imbalanced network traffic distribution. To address these two problems, a boosting random forest for network anomaly detection, called BRF, is proposed. The proposed method embeds the random forest model into Boosting training mechanism to enhance its classification ability for difficult samples. In the iterations, Random Forest is provided with relatively balanced and diverse training sets by dynamic resampling to alleviate the traffic imbalance problem. The effectiveness of BRF is demonstrated by multi-classification experiments on the NSL-KDD and UNSW-NB15 datasets. Compared with some shallow machine learning, deep learning, and ensemble learning methods, BRF has advantages in accuracy and time efficiency, which is a promising method for network anomaly detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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A Malware Analysis Method Based on Behavioral Knowledge Graph

A Malware Analysis Method Based on Behavioral Knowledge Grap...

引用

2022 International Conference on Electronic Information engineering and Computer Science, EIECS 2022

作者： Xu, Lianqiu Zhang, Chunyan Tang, Ke State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781510663312

In recent years, the rapid development of information technology has continuously brought risks and challenges to network security. How to effectively detect and classify malicious codes and avoid serious damage to information systems has become a current research focus. However, the existing malicious code analysis methods have insufficient semantic information and poor feature readability, making it difficult to discover the potential relationship between malicious codes. In order to solve these problems, this paper applies knowledge graph technology to the field of malicious code analysis, constructs malicious code behavior knowledge graph based on dynamic analysis design, and uses the representation learning model TransH to conduct knowledge graph embedding representation research, so as to use KNN algorithm to classify malicious sample families. The experimental results show that the method proposed in this paper has more advantages in the classification of malicious code families than the comparison model. © 2023 SPIE.

关键词： Knowledge graph

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ALEmu: A Framework for Application-layer Programs Emulation of Embedded Devices 4

ALEmu: A Framework for Application-layer Programs Emulation ...

引用

4th International Conference on Computer engineering and Application, ICCEA 2023

作者： He, Heng Xiong, Xiaobing Zhao, Yuntian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350347548

The difference between real devices and virtual environments causes a low success rate of application-layer program emulation when the firmware is operating in full-system emulation during the dynamic analysis of the firmware of embedded devices. In this paper, we propose ALEmu, an emulation framework for application-layer programs, which can effectively improve the emulation success rate of application-layer programs in embedded device firmware through automatic preprocessing of target programs, building configuration libraries, and hooking external program calls. When we test ALEmu on a variety of real-world devices, including routers and IP cameras, we find that it performs more successfully and accurately than the current state-of-the-art full-system emulation frameworks like Firmadyne and FirmAE. © 2023 IEEE.

关键词： Firmware

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An Intrusion Detection Feature Selection Method Based on Improved Mutual Information 6

An Intrusion Detection Feature Selection Method Based on Imp...

引用

6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Qi, Zan Fei, Jinlong Wang, Jue Li, Xue State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665460033

Feature selection methods for classification are crucial for intrusion detection techniques using machine learning. High-dimensional features in intrusion detection data affect computational complexity, consume more used resources and more time for data analysis, and the irrelevant and redundant features among them often hinder the performance of classifiers and mislead the classification task. Therefore, it is challenging to select more relevant features from intrusion detection data containing many such features. In this paper, we propose an efficient feature selection algorithm that first considers the correlation between features and the redundancy of pairs of features with respect to class labels based on an improved Pearson correlation coefficient, and later improves the evaluation function based on conditional mutual information to obtain a final subset of features with the goal of improving the classification rate and accuracy. The proposed feature selection method based on improved conditional mutual information is compared with three existing feature selection methods on the frequently studied public benchmark intrusion detection dataset NSL-KDD. The experimental results show that the features selected by the proposed method in this paper lead to a significant reduction in execution time while resulting in higher classification accuracy. © 2023 IEEE.

关键词： Feature Selection

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