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

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

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

引用

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

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

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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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RSM-PA:Research on Route Selection Method of Inter-domain Routing System Based on Path Availability 23

RSM-PA:Research on Route Selection Method of Inter-domain Ro...

引用

23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

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

ISBN: (纸本)9781665494571

The inter-domain routing system is the key infrastructure of the Internet. However, BGP does not consider the link state in the routing selection process. As a result, when the inter-domain routing system faces security incidents such as disasters and attacks, it is easy to cause the continuous propagation of link failures, resulting in a large-scale network disconnection and posing a considerable threat to the security of the Internet. The previous research mainly aims to enhance the protocol's safety, cannot change the original failure propagation mechanism, and cannot fundamentally solve cascading failure propagation. In response to this problem, this paper introduces SDN and proposes RSM-PA. This method perceives the available path topology by sharing the link state information in the region, provides a verification mechanism for the availability of the route in the routing process to avoid the link failure in the region due to overload. The comparison of routing results before and after the link state changes under the same experimental conditions shows that RSM-PA can effectively adjust the routing strategy according to the path availability, and lay a solid foundation for suppressing the propagation of cascading failures. © 2021 IEEE.

关键词： Network topology Heuristic algorithms Power system protection Routing Real-time systems Routing protocols Internet

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LOM: A Website Fingerprinting Defense Method based on Local Optimal Mutation 10

LOM: A Website Fingerprinting Defense Method based on Local ...

引用

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

作者： Wang, Jue Zhu, Yuefei Lin, Wei Li, Ding Tang, Ke State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Website Fingerprinting (WF) is a traffic analysis attack. The potential local adversary can infer the user's web activity by extracting implicit pattern information from traffic traces. Leveraging the latest advances in Deep Learning, the adversary has achieved 98% detection accuracy in closed-world tests. This poses a new challenge to the traditional WF defense methods. In this paper, we propose a new defense method, the proposed dissimilarity measure and local optimal mutation strategy greatly reduce the computational cost. Experimental results show that the method achieves the generation of adversarial traffic trace with shorter preparation times and better defensive effects. Moreover, it also has great potential in the face of more powerful attacks that may emerge in the future. © 2022 IEEE.

关键词： Websites

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MDFA: A Malware Detection Framework Based on Attention Mechanism with Bi-LSTM 4

MDFA: A Malware Detection Framework Based on Attention Mecha...

引用

4th International Conference on Applied Machine Learning, ICAML 2022

作者： Qi, Xuyan Wang, Huanwei Tang, Yonghe Liu, Wei Xie, Jiangtao State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665462655

As the existing malware intelligent detection methods have shortcomings and low accuracy of manual feature extraction and feature processing, a Malware Detection Framework with Attention mechanism based on Bi-directional Long Short-Term Memory (MDFA) was proposed. To training this method, We have collected 15, 893 malware and 10, 959 benign program samples and constructed a dataset. Then, the sample data was labeled and sanitized by determining the results based on VirusTotal analysis. Finally, the Bi-directional Long Short-Term Memory model injected into the attention mechanism was built and trained to complete the malware detection. Experimental results show that the accuracy of MDFA was improved by at least 2. 3%, and the recall rate was improved significantly compared with the existing methods, verifying the effectiveness of MDFA. © 2022 IEEE.

关键词： Long short-term memory

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Research on Memory Attacks and Defenses for Programmable Logic Controllers 4

Research on Memory Attacks and Defenses for Programmable Log...

引用

4th International Conference on Communications, Information System and Computer engineering, CISCE 2022

作者： Geng, Yangyang Liu, Ke Ma, Rongkuan Wei, Qiang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

Programmable logic controllers (PLCs) are the core of industrial control systems (ICS), which play an essential role in critical infrastructure. In recent years, significant real-world attacks against PLCs have emerged, causing devastating consequences to critical infrastructure. In this paper, we first introduce the architecture and memory system of PLC. We then systematically analyze the existing PLC memory attack methods and classify them into five types according to different attack vectors. Subsequently, we present three types of mainstream PLC memory attack detection methods and compare the detection ability against our proposed memory attacks. Finally, we discuss the challenges of existing PLC memory attack detection. © 2022 IEEE.

关键词： Programmable logic controllers

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A Research and Verification of MFC Framework-aware Virus Infection Technology 3

A Research and Verification of MFC Framework-aware Virus Inf...

引用

3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Guo, Wang Li, Yang Sha, Zihan Bu, Wenjuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665459112

Portable Executable (PE) virus technology is an important threat factor in the field of network security. With continuous upgrading of malware detection technology, virus infection technology will also gradually evolve from infecting the structure of the PE file to infecting the deeper structure of the program, and thus achieve higher level of stealthiness and confrontation. This paper proposes a structure-aware virus infection technology for Microsoft Foundation Classes (MFC) software framework, aiming to explore a new method of virus infection. This research aims to verify the virus infection method of MFC program framework perception through experiments, and then provides a solution to the new PE virus, which can help improve the current level of heuristic malware detection technology. © 2022 IEEE.

关键词： Viruses

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VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model for C/C++ Code 6

VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model ...

引用

6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Xiong, Zhe Dong, Weiyu School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350353174

Recently, deep learning techniques have received widespread attention in code vulnerability and vulnerability detection. However, there are some problems with current vulnerability detection techniques, firstly, because of the non-homogeneity of vulnerability categories and quantities, there are omission false positives and overfitting problems for vulnerabilities with a small amount of data. Second, the existing detection methods have limited ability to characterize the code, there is insufficient depth of characterization, does not contain all of its vulnerability characteristics or is interspersed with useless information. Finally, the current research does not use the powerful feature mining capability of pre-trained models and does not employ model fusion to increase the effectiveness of model vulnerability detection. To solve the above problems, this paper proposes a new deep learning based pre-trained model for code vulnerability detection tool VulD-CodeBERT. By fine-tuning CodeBERT, a code adaptation extension for pre-trained models developed by Microsoft, the CodeBERT model can solve the problem of not being able to learn the vulnerability patterns due to insufficient data, thus resulting in missed false alarms. The sequence output from the CodeBERT model are added to a BiLSTM network with an attention mechanism to mine the local key features of the vulnerabilities combined with the global features of the pooler output from the CodeBERT model to deeply mine the dependencies between the vulnerability statements and derive the probability of the existence of the vulnerability. For vulnerability types that are difficult to uncover captured features or the number of samples is not enough, a polynomial plain Bayesian classifier is used for detection, and finally the two models are combined to deeply mine the vulnerability features, thus solving the problem of difficult and small samples, and reducing the leakage rate and false alarm rate. Experimental results sh

关键词： C++ (programming language)

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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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