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2021 International Conference on Aviation Safety and Information Technology, ICASIT 2021

作者： Bu, Wenjuan Kang, Fei Zhao, Yuntian Xu, Jianbo State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Henan Zhengzhou450000 China

ISBN: (纸本)9781450390422

Based on the existing vulnerability exploitation detection technology, this paper analyzes the buffer overflow vulnerability exploitation process, and detects vulnerability triggering, protection mechanism bypassing and shellcode execution by dynamic binary instrumentation (DBI) monitoring program execution process;According to the analysis of vulnerability sample shellcode behavior, the method of recording instruction information and key API function information is designed based on instrumentation API function;A prototype platform of vulnerability sample transplantation and utilization based on dynamic binary is designed and implemented, and test analysis of 40 typical vulnerability samples is carried out to verify the effectiveness and feasibility of the system. © 2021 ACM.

关键词： Software testing

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Open Set Recognition for Encrypted Traffic using Intra-class Partition and Boundary Sample Generation 5

Open Set Recognition for Encrypted Traffic using Intra-class...

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

作者： Li, Xue Fei, Jinlong Qi, Zan Lv, Zhuo Jiang, Heng PLA Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China PLA Information Engineering University National Digital Switching System Engineering Technological Research Center Zhengzhou China

ISBN: (纸本)9781665479677

Existing deep learning-based encrypted traffic recognition methods can achieve high precision identification performance while protecting user privacy, but almost all of them focus on closed sets, in which training data and test data come from the same label space. However, the real network environment is open, and it is necessary for traffic identification systems to deal with previously unseen categories. To classify known categories at a fine granularity and identify unknown ones, we propose a novel open-set recognition method for encrypted traffic. The decision boundary sample generation method based on constrained Generative Adversarial Networks is presented to generate samples from low-density regions in the known data space. Furthermore, an end-to-end open-set classifier is built using the accurate and diverse simulative samples of unknown classes through intra-class partition and boundary sample generation. Finally, the effectiveness of the proposed method is validated through numerical experiments. © 2022 IEEE.

关键词： Deep learning

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Duen: Dynamic Ensemble Handling Class Imbalance in Network Intrusion Detection

SSRN

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SSRN 2023年

作者： Ren, Huajuan Tang, Yonghe Dong, Weiyu Ren, Shuai Jiang, Liehui State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou China

Network intrusion detection is an important technology for maintaining cybersecurity. The inherent difficulties co-existing in network traffic datasets, such as class imbalance, class overlapping, and noises, limit detection accuracy. However, existing research has only focused on the disproportion between classes. Considering these difficulties, a novel ensemble method is proposed for network intrusion detection called DUEN. Dynamic undersampling is incorporated into the Boosting framework to provide a relatively balanced training subset for each iteration to achieve a strong ensemble. In dynamic undersampling, the sampling number is determined based on data distribution learning to cope with the small sample overfitting problem caused by simple undersampling. The concept of classification hardness for integrating data difficulties is employed to identify samples as easy, borderline, and noisy types. Boundary samples importance enhancement mechanism is proposed for the undersampling process, preventing overfitting noise samples. The proposed sampling method does not involve distance calculation, achieving very low computational costs. Experiments show the effectiveness of DUEN with multiclass classification on two widely used datasets for evaluating intrusion detection. DUEN has high detection accuracy for both minority and majority class samples and a significant time efficiency advantage. The Macro.F1 of the NSL-KDD and UNSW-NB15 datasets reached 70.7% and 50.1%, achieving 6.32% and 1.42% improvement compared with the suboptimal network intrusion detection methods. Moreover, the experimental analysis shows that DUEN has strong generalization and robustness to noise samples. © 2023, The Authors. All rights reserved.

关键词： Intrusion detection

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First-principles investigation of microscopic mechanisms underlying hole mobilities in diamond, silicon, and germanium

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Physical Review B 2024年第15期110卷 155203页

作者： Qiao-Lin Yang Fan-Chen Meng Wu Li Zhi Wang Hui-Xiong Deng Su-Huai Wei Fan-long Ning Jun-Wei Luo School of Physics and Zhejiang Key Laboratory of Micro-Nano Quantum Chips and Quantum Control State Key Laboratory of Superlattices and Microstructures Research Computing and Data Eastern Institute for Advanced Study Center of Materials Science and Optoelectronics Engineering State Key Laboratory of Silicon and Advanced Semiconductor Materials

Silicon (Si) dominates the semiconductor industry but currently suffers from significant hole mobility degradation in advanced transistors due to its abnormally low hole mobility (505cm2/Vs), compared to its group IV neighbors, diamond and germanium (Ge) (both ∼2000cm2/Vs). While Ge's high mobility is understandable by its lighter effective hole mass due to its narrower direct bandgap at Γ point, diamond's superior hole mobility remains difficult to understand since its bandgap is ultrawide. Conventional wisdom attributes diamond's high mobility to reduced phonon scattering due to high acoustic phonon group velocity and optical phonon frequency, overcoming its heaviest effective mass. However, our study reveals that this mechanism alone is insufficient to explain the observed mobility. We demonstrate that the diamond's abnormally light effective hole mass is a primary factor contributing to its high mobility; otherwise, its hole mobility would be approximately nine times lower than Si's. The unexpectedly light mass in diamond stems from larger transition matrix elements, resulting from substantial electronic wave-function overlap between valence and conduction bands, remarkably overcoming the effect of diamond's large bandgap. Furthermore, our investigation uncovers a unique feature in diamond's valence band structure: an inherent strain-enhanced mobility characteristic, where the top valence band manifests as a light-hole (lh) state along certain crystallographic directions (e.g., [100]), instead of the typical heavy-hole (hh) state. Contrary to prevailing assumptions, we observe a strong coupling between holes and transverse acoustic (TA) phonons, and even TA scattering dominates over longitudinal acoustic (LA) scattering in both diamond and Si, challenging the long-held belief that TA scattering is negligible. Our calculations yield a dilation deformation potential of 3.45 eV for diamond, significantly diverging from previously reported values, e.g., 7 or −35 eV.

关键词： Electrical conductivity Electron-phonon coupling Semiconductors Density functional calculations Wannier function methods

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Performance Verification of Smith Predictor Control Using IMC Scheme via Chemical Reaction Networks and DNA Strand Displacement Reaction 9

Performance Verification of Smith Predictor Control Using IM...

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9th IEEE Smart World Congress, SWC 2023

作者： Yao, Jingwang Lv, Hui Xiao, Yijun Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350319804

Biochemical process control is often achieved through feedback control strategies for theoretical analysis and optimization of biochemical reaction processes. Here, the design improves the robustness of the biochemical process by taking advantage of the Smith prediction structure and internal mode control within the chemical reaction network theory framework to intervene against the time delays and perturbations present in the biochemical process. First, an IMC representation of Smith prediction is constructed using the presence of parameter rectification of the control method, and then an IMC-MSP control scheme is proposed. A first-order time-lag controlled object is established in the construction of the IMC-MSP control scheme. Besides, CRNs and kinetic representations are established based on the control structure to facilitate the evaluation of the control system performance. In this paper, CRNs representations of parabolic functions as signal inputs are defined for the first time and optimized for stepping signals to facilitate the design and study of biomolecular circuits. Notably, for the CRNs-based IMC-MSP control scheme, on the one hand, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from multiple perspectives of time delays, external perturbations, and filter tuning parameters;on the other hand, using DNA strand displacement is reflected to verify the feasibility of the whole scheme. © 2023 IEEE.

关键词： Chemical reaction networks (CRNs) DNA strand displacement (DSD) Internal model control (IMC control) Smith predictor

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SoK: A Survey on Redundant Execution Technology

SoK: A Survey on Redundant Execution Technology

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advanced computing and Endogenous Security, International Conference on

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

ISBN: (纸本)9781665424509

Redundant execution technology is one of the effective ways to improve the safety and reliability of computer systems. By rationally configuring redundant resources, adding components with the same function, using the determined redundant execution logic to coordinate and efficiently execute synchronously can effectively ensure high availability of the machine and system. Fault-tolerant is based on redundant execution, which is the primary method of dealing with system hardware failures. Recently, multi-threading redundancy has realized the continuous development of fault-tolerant technology, which makes the processing granularity of the system tolerate random failure factors gradually reduced. At the same time, intrusion tolerant technology has also been continuously developed with the emergence of multi-variant execution technology. It mainly uses the idea of dynamic heterogeneous redundancy to construct a set of variants with equivalent functions and different structures to complete the detection and processing of threats outside the system. We summarize the critical technologies of redundant execution to achieve fault tolerance and intrusion tolerance in recent years, sorts out the role of redundant execution in the development process from fault tolerance technology to intrusion tolerance technology, classify redundant execution technologies at different levels, finally point out the development prospects of redundant execution technology in multiple application fields and future technical research directions.

关键词： Fault tolerant systems Redundancy Cyberspace Games Computer architecture Hardware Safety

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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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A Software/Hardware Co-design Local Irregular Sparsity Method for Accelerating CNNs on FPGA 22

A Software/Hardware Co-design Local Irregular Sparsity Metho...

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Workshop Proceedings of the 51st International Conference on Parallel Processing

作者： Jiangwei Shang Zhan Zhang Chuanyou Li Kun Zhang Lei Qian Hongwei Liu Harbin Institute of Technology China Southeast University China State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450394451

Convolutional neural networks (CNNs) have been widely used in different areas. The success of CNNs comes with a huge amount of parameters and computations, and nowaday CNNs still keep moving toward larger structures. Although larger structures often bring about better inference accuracy, the increasing size also slows the inference speed down. Recently, various parameter sparsity methods have been proposed to accelerate CNNs by reducing the number of parameters and computations. Existing sparsity methods could be classified into two categories: unstructured and structured. Unstructured sparsity methods easily cause irregularity and thus have a suboptimal speedup. On the other hand, the structured sparsity methods could keep regularity by pruning the parameters following a certain pattern but result in low sparsity. In this paper, we propose a software/hardware co-design approach to bring local irregular sparsity into CNNs. Benefiting from the local irregularity, we design a row-wise computing engine, RConv Engine, to achieve workload balance and remarkable speedup. The experimental results show that our software/hardware co-design method can achieve a 10.9x speedup than the state-of-the-art methods with a negligible accuracy loss.

关键词： field-programmable gate arrays (FPGAs) hardware accelerator convolutional neural network software/hardware co-design sparsity method

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A Relation-Oriented Method for Joint Entity and Relation Extraction Based on Neural Network 21

A Relation-Oriented Method for Joint Entity and Relation Ext...

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5th International Conference on Electronic Information Technology and Computer engineering, EITCE 2021

作者： Hu, Hongwei Yin, Meijuan Liu, Xiaonan Key Laboratory of Cyberspace Situation Awareness of Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450384322

Entity and relation extraction is a basic task of information extraction in natural language processing. At present, Entity and relation extraction based on artificial intelligence has been widely studied, but most methods adopt the idea of identifying the entity first and then extracting the relation, which suffers from the problems of overlapping triple and entity redundancy. We propose a Relation-Oriented method for Joint Entity and Relation Extraction (ROJER), which first extracts the relation types contained in the text through the relation extraction module, then the pre-extracted relation types are integrated into the entity recognition module, reduce the focus on irrelevant entities and avoid extracting redundant entities. Then the entity pairs corresponding to the extracted relation types are identified to tackle the overlapping triple problem and finally extract all the relation triples. Experiments on the DuIE dataset show that the F1 score of ROJER reaches 78.4%, which is 1.2% higher than the CasRel model, confirming the validity of our method. © 2021 ACM.

关键词： Artificial intelligence

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

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

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International Conference on Communications, Information System and Computer engineering (CISCE)

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

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

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： Deep learning Adaptation models Codes Attention mechanisms Computational modeling Feature extraction Polynomials

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