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7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Tang, Ke Liu, Fudong Shan, Zheng Zhang, Chunyan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811659393

Information technology facilitates people’s lives greatly, while it also brings many security issues, such as code plagiarism, software in-fringement, and malicious code. In order to solve the problems, reverse engineering is applied to analyze abundant binary code manually, which costs a lot of time. However, due to the maturity of different obfuscation techniques, the disassembly code generated from the same function differs greatly in the opcode and control flow graph through different obfuscation options. This paper propose a method inspired by natural language processing, to realize the semantic similarity matching of binary code in basic block granularity and function granularity. In the similarity matching task of binary code obtained by different obfuscation options of LLVM, the indicator reaches 99%, which is better than the existing technologies. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Genes

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Research on key Technology of Industrial Network Boundary Protection based on Endogenous Security 4

Research on Key Technology of Industrial Network Boundary Pr...

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4th IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2021

作者： Yu, Fei Wei, Qiang Geng, Yangyang Wang, Yunchao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9781728185347

Industrial network boundary protection equipment faces threats from attackers when protecting the industrial control system network. The similarity and static characteristics caused by large-scale and long-term deployment determine that it could only defend against known attacks but could not deal with unknown APT threats, which leads to the breakthrough of one defense line is equivalent to the breakthrough of all defense lines and may bring challenges to industrial production safety. This paper proposes a mimic defense model of industrial isolation gateway based on endogenous security. With the dynamic scheduling mechanism to transform the attack surface, the gateway selects multiple heterogeneous filter executors to process the same packet simultaneously. By comparing the processing results of each executor, anomaly detection is carried out to realize the dynamic defense of the industrial isolation gateway. The experimental results show that the industrial isolation gateway based on mimic architecture can significantly increase the difficulty of backdoor utilization, such as paralysis, rule tampering, and information theft, and effectively defend the industrial control system from the threats caused by the backdoors and vulnerabilities of the isolation gateway while exerting the normal boundary protection function. © 2021 IEEE.

关键词： Anomaly detection

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A deep learning driven hybrid beamforming method for millimeter wave MIMO system

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Digital Communications and Networks 2023年第6期9卷 1291-1300页

作者： Jienan Chen Jiyun Tao Siyu Luo Shuai Li Chuan Zhang Wei Xiang The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengduSichuan611731China National Mobile Communications Research Laboratory Southeast UniversityPurple Mountain LaboratoriesNanjing210096China School of Computing Engineering and Mathematical SciencesLa Trobe UniversityMelbourneVIC3086Australia

The hybrid beamforming is a promising technology for the millimeter wave MIMO system,which provides high spectrum efficiency,high data rate transmission,and a good balance between transmission performance and hardware *** most existing beamforming systems transmit multiple streams by formulating multiple orthogonal ***,the Neural network Hybrid Beamforming(NHB)adopts a totally different strategy,which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation *** by the Deep Learning(DL)hybrid beamforming,in this work,we propose a DL-driven nonorthogonal hybrid beamforming for the single-user multiple streams *** first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate(BER)performance than the orthogonal hybrid beamforming even with the optimal power *** by the NHB,we propose a new DL-driven beamforming scheme to simulate the NHB behavior,which avoids time-consuming neural network training and achieves better BERs than traditional hybrid ***,our simulation results demonstrate that the DL-driven nonorthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of subconnected schemes and imperfect Channel State Information(CSI).

关键词： Hybrid beamforming Neural network Deep learning driven Non-orthogonal beamforming

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NAS Honeypot Technology Based on Attack Chain 10th

NAS Honeypot Technology Based on Attack Chain

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10th International Conference on Computer engineering and Networks, CENet 2020

作者： Liu, Bing Shu, Hui Kang, Fei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811584619

With the wide application of network attached storage (NAS), the security problem is becoming more and more serious, together with the increasingly fierce attacks against NAS devices of different manufacturers. In order to better capture various types of attacks against NAS devices and detect security threats in time, this paper proposes a solution named NAS honeypot, mining the potential security threats through modeling and analyzing the NAS threats. Then a NAS honeypot based on device virtualization technology and depth monitoring and attack induction was designed on the basis of the construction NAS attack chain. Experimental results show that the NAS honeypot can effectively capture, record and analyze network attacks against multi-types NAS devices with a strong guiding effect in mastering popular attack method of NAS devices and alleviating the security threats of NAS. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Security systems

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TexCIL: Text-Guided Continual Learning of Disease with Vision-Language Model

TexCIL: Text-Guided Continual Learning of Disease with Visio...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Wentao Zhao, Defeng Zheng, Wei-Shi Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Shenzhen China Moe Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou China

ISBN: (纸本)9798350386226

Current intelligent diagnostic systems often catastrophically forget old knowledge when learning new diseases only from the training dataset of the new diseases. Inspired by human learning of visual classes with the effective help of language, we propose a continual learning framework based on a pre-trained visual-language model (VLM) without storing any image of previously learned diseases. In this framework, textual prior knowledge of each new disease can be obtained by utilizing the frozen VLM's text encoder, and then used to guide the visual learning of the new disease. This framework innovatively utilizes the textual prior knowledge of all previously learned diseases as out-of-distribution (OOD) information to help differentiate currently being-learned diseases from others. Extensive empirical evaluations on both medical and natural image datasets confirm the superiority of the proposed method over existing state-of-the-art methods in continual learning of new visual classes. The source code is available at https://***/OpenMedIA/TexCIL. © 2024 IEEE.

关键词： Contrastive Learning

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Malicious Traffic Detection Model Refactor Method Based on Adaptive Sample Generation 5

Malicious Traffic Detection Model Refactor Method Based on A...

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5th IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Jiao, Liang Fei, Jinlong Zhao, Min State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Tianjin University of Science and Technology School of Electronic Information and Automation Tianjin China

ISBN: (纸本)9781665479677

As machine learning models are widely used in malicious traffic detection, model security has become an important research topic. Aiming at the problems that the model refactor attack methods need a large amount of prior knowledge of the target model, rely on a large amount of real data, and have a narrow scope of application, a model refactor method based on adaptive sample generation was proposed. By means of an adaptive sample generation algorithm based on graph retrieval, a small number of real samples are used to quickly generate generated samples uniformly distributed in the input domain. The boundary of the refactored model is corrected according to the decision of the target model, the refactored model is iteratively trained by combining the network traffic feature selection method and the model hyperparameter optimization method, and the model refactor attack is finally realized. The refactor attack experiment against five malicious traffic detection machine learning models shows that the refactor rate and accuracy of the refactored model increase gradually with the increase of iteration rounds, and the refactor rate is more than 90%, using only the target model output label. © 2022 IEEE.

关键词： Iterative methods

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Vulnerability Digging for Software-Defined Network Controller Using Event Flow Graph Analysis

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Security and Communication Networks 2022年 2022卷

作者： Wu, Zehui Zhang, Wenbin Wang, Yunchao Yan, Chenyu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Software-defined network (SDN) controllers, the core of SDN network architecture, need to deal with network events of the whole network, which has huge program state space and complex logic dependency, with security issues related. Vulnerabilities in the SDN controller can paralyze the whole network. Existing controller testing methods are difficult to dig into the hidden logic vulnerability for their ignorance of the complex events interactions among controllers, apps, and data plane inputs. Different from file processing software, network software is driven by events, and the event flow can more accurately and comprehensively reflect the execution process. In this work, we propose an SDN controller vulnerability digging method based on event flow graph analysis. The proposed method consists of three main steps: first, we execute the instrumented controller in a normal environment and generate event flow graphs and then extract their features as reference. Second, we generate and execute test cases using the fuzzing method and dig the newly built event flow graphs with potential vulnerabilities. Finally, we manually examine and validate the potential vulnerabilities. To accurately discover abnormal subgraphs, we utilize graph feature extraction and analysis technologies, such as graph mining and clustering, to distinguish the normal graph and abnormal graph. We implement our method on the Ryu controller and compare it with other SDN testing methods, such as BEADS and Delta. The evaluation indicates that our method uncovered three new vulnerabilities that other methods failed to find. © 2022 Zehui Wu et al.

关键词： Controllers

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Directed Association-based User Identification Algorithm 2

Directed Association-based User Identification Algorithm

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2nd International Conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2021

作者： Jiang, Qiuyan Gong, Daofu Liu, Fenlin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Science and Information Qingdao Agricultural University Qingdao266001 China

This paper studies the social network with directed association semantics, and proposes an unsupervised user identification algorithm (DAUM-P). The algorithm construct a user associations graph based on multiple types of directed user behaviors, and define directed associations;then, walk based on directed associations to obtain a walking sequence, and then combine the network embedding model to learn user characteristics vector representation;finally, the identification result is given based on the representation vector distance. Finally, the experiments show that the user identification effect is better than that of the baseline method. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Semantics

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Multi Association Semantics-Based User Matching Algorithm Without Prior Knowledge

Multi Association Semantics-Based User Matching Algorithm Wi...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Jiang, Qiuyan Gong, Daofu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Science and Information Qingdao Agricultural University Qingdao266001 China

ISBN: (纸本)9789811669620

Cross-network user matching is one of the basic issues for realizing social network data integration. Existing research based on structure features provides a good matching method for nodes with high-degree, but ignores the matching of nodes with low values. This paper proposes an unsupervised cross-network user matching algorithm based on association semantics to solve the problem of cross-network user matching. It can effectively solve the low-degree user matching problem when the quality of the text type attributes of network users cannot be guaranteed. First, build a social network graph based on multiple types of user behaviors, and define different association semantics according to different behaviors;then, walk based on the association semantics to obtain user node sequences with association semantics, and then combine the network embedding model to learn the user feature vector representation is obtained;finally, the user similarity is measured based on the user vector similarity, and then cross-network user matching is realized. The highlights of this paper are two folds. (1) We treat the association as nodes. (2) Social network user graphs with association semantics can enrich structure feature;walks based on association semantics can obtain walks that are closer to natural language short sentences. The experimental results show that the proposed method can effectively match user with similar behavior characteristics without any prior knowledge, and can effectively achieve the matching of low-degree nodes in heterogeneous networks. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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