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2021 International Conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2021

作者： Wang, Hongmin Wei, Qiang Xie, Yaobin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9781643682549

The safe and stable operation of power system is related to the national economy and people's livelihood of the whole country. Blackouts are almost always caused by cascading failures. This paper first analyzes the global blackouts in the past 20 years, and points out that the scale and probability distribution of blackouts obey the power-law relationship. Secondly, it is concluded that transmission line fault, bus fault, large-scale power flow transfer and relay protection device misoperation are the main factors causing cascading failures. Then, the five stages of cascading failure evolution are analyzed, which are slow successive interruption process, fast successive interruption process, transient oscillation, avalanche blackout and long recovery. Finally, the research results of cascading failure at home and abroad in recent years are analyzed from three research points: small world network, topological network and two-layer coupling network. © 2022 The authors and IOS Press.

关键词： Cyber Physical System

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Recommender system based on adaptive threshold filtering GCN

Recommender system based on adaptive threshold filtering GCN

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2022 International Conference on Neural Networks, Information, and Communication engineering, NNICE 2022

作者： Qiao, Meng Gui, Hairen Tang, Ke State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China

ISBN: (纸本)9781510655171

We introduce the AT-GCN (Adaptive Threshold filtering Graph Convolutional Neural network model). AT-GCN is a recommendation model based on graph structure. Compared with the commonly used graph structure recommendation model, AT-GCN can effectively solve the problem of edge representation and information transfer, and improve the recommendation effect. In the experimental part, several groups of experiments were carried out on AT-GCN, and the above conclusions were finally verified by the experimental results. © 2022 SPIE.

关键词： Recommender systems

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Research on key Techniques for Reverse engineering of Deep Learning Models for x86 Executable Files 24

Research on Key Techniques for Reverse Engineering of Deep L...

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2024 7th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2024

作者： Shi, Meng Lin, Wei Deng, Wenbo College of Cyberspace Security Information Engineering University Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Department of Cyberspace Security Henan Zhengzhou China

ISBN: (纸本)9798400707254

In recent years, with the continuous development of machine learning and deep learning, their related applications have gradually appeared in our field of vision, showing explosive growth. A deep learning compiler optimizes a deep learning model, compiles it, and generates a binary file. When this binary file is placed into an edge device, the device can perform model inference. However, the generated binary file shares characteristics with ordinary executable files, meaning that important information within the binary file could potentially be leaked through disassembly. This paper leverages this critical characteristic by reverse-engineering the binary library files of the model to extract key parameters and thereby restore the model’s framework. Using model library files deployed on edge devices, combined with disassembly, model prediction, dynamic analysis, and other methods, this study successfully recovers the model’s operators, topology, and network hyperparameters. Experimental evaluations demonstrate that the framework of the model can be accurately reconstructed, and the substitute model trained using the original dataset achieves accuracy comparable to that of the original model. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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A survey of proactive defense in industrial control system

A survey of proactive defense in industrial control system

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2022 International Conference on Electronic Information Technology, EIT 2022

作者： Liu, Ke Wu, Qianqiong Geng, Yangyang Ma, Rongkuan Wei, Qiang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9781510655096

With the development of Industrial 4.0, the popularity of information technology (IT) systems in industrial control systems (ICSs) has brought great cyber-attack risk. The proactive defense technology, an important defense approach, plays a vital role in ICSs. This paper introduces the definition, taxonomy and application of proactive defense in ICSs. We divide the existing ICS proactive defense approaches into three categories and summarize their technical characteristics. Finally, the application prospects and open issues of proactive defense in ICS are summarized. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Cybersecurity

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Binary vulnerability mining technology based on neural network feature fusion 5

Binary vulnerability mining technology based on neural netwo...

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5th International Conference on advanced Electronic Materials, Computers and Software engineering, AEMCSE 2022

作者： Han, Wenjie Pang, Jianmin Zhou, Xin Zhu, Di State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

The high complexity of software and the diversity of security vulnerabilities have brought severe challenges to the research of software security vulnerabilities Traditional vulnerability mining methods are inefficient and have problems such as high false positives and high false negatives, which can not meet the growing needs of software security. To solve the above problems, this paper proposes a binary vulnerability mining technology based on neural network feature fusion. Firstly, this method constructs binary vulnerability data sets containing multiple vulnerability types, then decompile them to the pcode intermediate language level, and then extracts relevant feature vectors from binary vulnerability data sets according to Bert fine tuning model and bilstm model respectively. In order to fully obtain the semantic information of vulnerabilities, this method standardized the two, fused them, and carried out relevant experiments. The experimental results show that the accuracy of vulnerability detection on SARD data set is 96.92%, which is higher than other binary vulnerability detection methods based on neural network. © 2022 IEEE.

关键词： Deep neural networks

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Analysis of security capability of MVX based on probabilistic attack and defense models 3

Analysis of security capability of MVX based on probabilisti...

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3rd International Conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Liu, Zijing Zhang, Zheng Xi, Ruicheng Zhu, Pengzhe State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9781510661240

Multi Variant eXecution (MVX) is a security defense technique that uses software diversity to protect system from attacks. MVX improves security capability by enhancing system endogenous security compared to traditional passive defense techniques. However, the current MVX technique lacks formal theoretical analysis and cannot effectively assess the overall security of the system. To address the constraint relationship between complex attack means and dynamic defense environment, we construct a novel atomic combination attack chain model, which decomposes macro attack means into single atomic attack behaviors and provides theoretical support for analyzing the security capability of dynamic systems. Then, the defense model of the MVX system is established, and the defense model's security capability is analyzed using the attack model. Finally, the advantages and shortcomings of the security defense capability of the MVX system are evaluated based on a typical kernel attack example, and system optimization improvement measures are proposed. © 2022 SPIE.

关键词： Network security

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A Bidirectional Data Packet Tampering Method for Linux System

A Bidirectional Data Packet Tampering Method for Linux Syste...

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2022 International Conference on Network Communication and Information Security, ICNCIS 2022

作者： Lv, Siou Yang, Pengfei Liu, Shengli State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9781510661134

Network data security is very important for each user and service provider, and every process of network data transmission is at risk of being tampered with. In this paper, we proposed a bidirectional tampering method of network data for Linux system. In this method we uses the combination of Netfilter and iptables, and uses the NetfilterQueue module in Python to tamper with the packets entering and leaving a Linux host. This tampering operation is transparent to upper users and may cause domain name redirection or many other serious consequences which threaten the security of hosts. Finally, the feasibility of this tampering method is verified by experiments. © 2022 SPIE.

关键词： Python

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Haica: A High Performance computing & Artificial Intelligence Fused computing Architecture 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Zhengbo Zheng, Fang Guo, Feng Yu, Qi Chen, Zuoning Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Chinese Academy of Engineering Beijing China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

In recent years, HPC and AI fusion, which refers to applying AI technology to traditional HPC applications, has become a new trend. HPC and AI fusion requires supports for multiple precisions used in both domains. While, supporting multiple precisions in a single computing unit is not easy. Prior research typically targets on modifying a high-precision FMA to support low precisions. However, such efforts suffer from disadvantages of increased latency and limited compute throughput for low-precision operations, high area and power overhead, and limited support of new data formats appeared in AI domain. To address this issue, we propose Haica, a double-precision FMA and low-precision systolic array fused architecture, to achieve HPC and AI fusion. Our work has two innovations. First, we propose a low-cost multiple-low-precision FMA by taking advantage of the commonality among FP16, BF16, and TF32. Second, inspired by the idea of splicing high precision with low precision, we replace the multiply and merge modules in a double-precision FMA with a modified systolic array that is composed of the proposed low-precision FMAs. We implement the logic design of Haica using RTL and evaluate its overhead. Results show that compared to the naive combination of a double-precision FMA and single-half-mixed-precision systolic array, Haica provides extra support for BF16 and TF32 with only 7.87% area and 33.26% power overhead. This demonstrates that Haica achieves HPC and AI fusion in a cost-effective manner. © 2023, Springer Nature Switzerland AG.

关键词： Artificial intelligence

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An Improved Authenticated key Agreement Protocol for IoT and Cloud Server 5th

An Improved Authenticated Key Agreement Protocol for IoT an...

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5th International Conference on Security and Privacy in New computing Environments, SPNCE 2022

作者： Ma, Yongliu Ma, Yuqian Cheng, Qingfeng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Strategic Support Force Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9783031306228

The internet of things (IoT) is a complex network system with applications in all walks of life. However, there are various risks in the process of information transmission between IoT devices and servers. Recently, research on the security of authenticated key agreement (AKA) protocols in the IoT environment has gradually increased. Iqbal et al. proposed an AKA protocol between IoT and cloud servers and proved that it was secure under the eCK model. This paper shows that the Iqbal et al.’s protocol has two security flaws, which are resisting ephemeral key leakage attack and key compromise impersonation attack, and proposes a new AKA protocol in the IoT environment. Through the security proof and formal analysis, it is proved that the new protocol is secure under the eCK model. Comparing the protocol proposed in this paper with other similar protocols, it is found that the protocol in this paper achieves a balance between security performance and communication consumption. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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