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

Dynamic Resampling Based Boosting Random Forest for Network...

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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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Linearized Transformed L1 Galerkin FEMs with Unconditional Convergence for Nonlinear Time Fractional Schr¨odinger Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 348-369页

作者： Wanqiu Yuan Dongfang Li Chengjian Zhang School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

A linearized transformed L1 Galerkin finite element method(FEM)is presented for numerically solving the multi-dimensional time fractional Schr¨odinger *** optimal error estimates of the fully-discrete scheme are *** error estimates are obtained by combining a new discrete fractional Gr¨onwall inequality,the corresponding Sobolev embedding theorems and some inverse *** the previous unconditional convergence results are usually obtained by using the temporal-spatial error spitting *** examples are presented to confirm the theoretical results.

关键词： Optimal error estimates time fractional Schr¨odinger equations transformed L1 scheme discrete fractional Gr¨onwall inequality

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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A Malware Analysis Method Based on Behavioral Knowledge Graph

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

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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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Improving Performance of Virtual Machine Covert Timing Channel Through Optimized Run-Length Encoding

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Journal of Computer Science & Technology 2023年第4期38卷 793-806页

作者：王翀陈荣亮古亮 Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Sangfor Technologies Inc. Shenzhen 518055China Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen 518055China

With its wider acceptability,cloud can host a diverse set of data and applications ranging from entertainment to personal to *** foundation of cloud computing is based on virtual machines where boundaries among the application data are very thin,and the potential of data leakage exists all the *** instance,a virtual machine covert timing channel is an aggressive mechanism to leak confidential information through shared components or networks by violating isolation and security policies in *** performance of a covert timing channel(covert channel)is crucial to adversaries and attempts have been made to improve the performance of covert timing channels by advancing the encoding mechanism and covert information *** promising,the redundancy of the covert message is mainly *** paper applies three encoding schemes namely run-length,Huffman,and arithmetic encoding schemes for data compression of a virtual machine covert timing channel by exploiting ***,the paper studies the performance of such channels according to their ***,we show that these encoding schemes still contain redundancy in a covert channel scenario,and thereby a new encoding scheme namely optimized Runlength encoding(OptRLE)is presented that greatly enhances the performance of a covert timing *** optimizations schemes adopted by OptRLE are also discussed,and a mathematical model of the behavior of an OptRLE-based covert timing channel is *** theoretical capacity of a channel can be obtained using the proposed *** analysis reveals that OptRLE further improves the performance of a covert timing channel,in addition to the effects of the *** result shows how OptRLE affects the size of covert data and the capacity of covert timing channels,and why the performance of the covert timing channel is improved.

关键词： covert storage channel information security covert channel threat evaluation anti-detection criterion covert channel restriction

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An Improved key Mismatch Attack on Kyber 7

An Improved Key Mismatch Attack on Kyber

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

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

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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...

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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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Critical Variable Guided Mutation for Directed Greybox Fuzzing

Critical Variable Guided Mutation for Directed Greybox Fuzzi...

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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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