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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Shun-Hang Li Gang Zhou Zhi-Bo Li Ji-Cang Lu Ning-Bo Huang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

As the intelligent software, the development and application of large language models are extremely hot topics recently, bringing tremendous changes to general AI and software industry. Nonetheless, large language models, especially open source ones, incontrollably suffer from some potential software quality issues such as instability, inaccuracy, and insecurity, making software testing necessary. In this paper, we propose the first solution for functional testing of open large language models to check full-scene availability and conclude empirical principles for better steering large language models, particularly considering their black box and intelligence properties. Specifically, we focus on the model’s causal reasoning ability, which is the core of artificial intelligence but almost ignored by most previous work. First, for comprehensive evaluation, we deconstruct the causal reasoning capability into five dimensions and summary the forms of causal reasoning task as causality identification and causality matching. Then, rich datasets are introduced and further modified to generate test cases along with different ability dimensions and task forms to improve the testing integrity. Moreover, we explore the ability boundary of open large language models in two usage modes: prompting and lightweight fine-tuning. Our work conducts comprehensive functional testing on the causal reasoning ability of open large language models, establishes benchmarks, and derives empirical insights for practical usage. The proposed testing solution can be transferred to other similar evaluation tasks as a general framework for large language models or their derivations.

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A Selection Strategy for Network Security Defense Based on a Time Game Model

A Selection Strategy for Network Security Defense Based on a...

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2021 International Conference on Digital Society and Intelligent Systems, DSInS 2021

作者： Sun, Pengyu Zhang, Hengwei Ma, Junqiang Li, Chenwei Mi, Yan Wang, Jindong Key Laboratory of Information Security of Henan Provincial State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665406307

Current network assessment models often ignore the impact of attack-defense timing on network security, making it difficult to characterize the dynamic game of attack-defense effectively. To effectively manage the network security risks and reduce potential losses, in this article, we propose a selection strategy for network defense based on a time game model. By analyzing the attack-defense status by analogy with the SIR infectious disease model, construction of an optimal defense strategy model based on time game, and calculation of the Nash equilibrium of the the attacker and the defender under different strategies, we can determine an optimal defense strategy. With the Matlab simulation, this strategy is verified to be effective. © 2021 IEEE.

关键词： Network security

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Effect of thickness variations of lithium niobate on insulator waveguide on the frequency spectrum of spontaneous parametric down-conversion

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Chinese Physics B 2021年第11期30卷 268-273页

作者： Guang-Tai Xue Xiao-Hui Tian Chi Zhang Zhenda Xie Ping Xu Yan-Xiao Gong Shi-Ning Zhu National Laboratory of Solid State Microstructures School of PhysicsSchool of Electronic Science and EngineeringCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of ComputingNational University of Defense TechnologyChangsha 410073China

We study the effect of waveguide thickness variations on the frequency spectrum of spontaneous parametric downconversion in the periodically-poled lithium niobate on insulator(LNOI)*** analyze several variation models and our simulation results show that thickness variations in several nanometers can induce distinct effects on the central peak of the spectrum,such as narrowing,broadening,and *** also prove that the effects of positive and negative variations can be canceled and thus lead to a variation-robust feature and an ultra-broad *** study may promote the development of on-chip photon sources in the LNOI platform,as well as opens up a way to engineer photon frequency state.

关键词： lithium niobate on insulator spontaneous parametric down-conversion waveguide thickness variation frequency spectrum

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Feature Rotation Invariance Learning for Point Cloud Analysis 10

Feature Rotation Invariance Learning for Point Cloud Analysi...

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10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

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Timing Strategy for Active Detection of APT Attack Based on FlipIt Model and Q-learning Method

Timing Strategy for Active Detection of APT Attack Based on ...

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2021 International Conference on Digital Society and Intelligent Systems, DSInS 2021

作者： Liu, Zhilin Zhang, Hengwei Sun, Pengyu Mi, Yan Zhang, Xiaoning Wang, Jindong Key Laboratory of Information Security of Henan Provincial State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665406307

At present, APT attack detection mainly focuses on the specific realization of detection technology, and there is little research on the timing strategy for active detection of APT attack. This paper focuses on the problem of APT dynamic detection with timing decision intelligence. We use the FlipIt game model and reinforcement learning method of Q-learning algorithm, consider the APT attack events as an exponentially distributed random process, and the defender using the attacker's last move time (LM-defender) to learn his optimal timing strategy using Q-Learning algorithm. The defender's reinforcement learning mechanism for the timing of the APT attack action and outputs its timing selection strategy. Simulation experiments show that compared with the existing greedy algorithm, the timing strategy based on the Q-Learning algorithm does not require the attacker's prior information under the premise, it can achieve a higher average benefit than the greedy strategy. This result provides a theoretical reference for the design of timing strategy to defend APT attack intelligently and efficient. © 2021 IEEE.

关键词： Reinforcement learning

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Network Attack Intention Recognition Based on Signaling Game Model and Netlogo Simulation

Network Attack Intention Recognition Based on Signaling Game...

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2021 International Conference on Digital Society and Intelligent Systems, DSInS 2021

作者： Zhang, Xiaoning Zhang, Hengwei Li, Chenwei Sun, Pengyu Liu, Zhilin Wang, Jindong State Key Laboratory of Mathematical Engineering and Advanced Computing Key Laboratory of Information Security of Henan Provincial Zhengzhou China

ISBN: (纸本)9781665406307

With the development of attack technology, identifying the real intention of network attack and avoiding further deterioration of security situation has become a hot research topic in recent years. This paper proposes an attack intention recognition method based on signaling game model. Firstly, we generate attack intention hypotheses, and then calculate the probability of each attack intention by solving game equilibrium. Experiments show that the method can effectively improve the accuracy of attack intention recognition from the perspective of the revenue. © 2021 IEEE.

关键词： Deterioration

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On the Cycle Structure of Some Nonlinear Feedback Shift Registers

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Chinese Journal of Electronics 2025年第4期23卷 801-804页

作者： Zhongxiao Wang Hong Xu Wenfeng Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we partly determine the cycle structure of two types of Nonlinear feedback shift registers (NFSRs). Based on these results, the cycle structure of a class of NFSRs with symmetric feedback functions can be completely characterized. Furthermore, an alternative proof of Kjeldsen's results is presented. Compared with the original proof based on abstract algebra theory, ours is straightforward and easy to understand.

关键词： Abstract algebra Shift registers Cryptography

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Polytopic Attack on Round-Reduced Simon32/64 Using Deep Learning 16th

Polytopic Attack on Round-Reduced Simon32/64 Using Deep Lear...

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16th International Conference on Information Security and Cryptology, Inscrypt 2020

作者： Su, Heng-Chuan Zhu, Xuan-Yong Ming, Duan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9783030718510

In CRYPTO 2019, Gohr uses the residual network technology of artificial intelligence to build a differential distinguisher, and attacks the reduced-round Speck32/64. We tried this method to recover the keys for ten-round Simon32/64. In this paper, we have three innovations. First, we construct polytope neural network distinguisher. On eight-round Simon32/64, polytope neural network distinguisher could increase the success rate of three neural network distinguishers with 0.76 success rate to 0.92. Second, we propose an attack on Simon32/64 based on the combination of the probability of differential path and polytope neural network distinguisher. This method can only increase the computational complexity of the chosen data as the number of rounds increases. Nine-round polytope neural network distinguisher is used to filter out data, whether it is what we want. Eight-round neural distinguisher is used to recover the final round key. The computational complexity of key recovery on the final key of eleven-round Simon32/64 is 233.4. Third, we propose an attack called Bayesian key Research with Error. With this attack, the computational complexity of key recovery on the final key of eleven-round Simon32/64 is 230.9. In our paper, the main idea is combining polytope differences with neural networks. By constructing polytope differential neural network distinguisher, we make a key recovery attack. In order to increase the number of rounds, we first used brute force attack and then proposed Bayesian key Research with Error. We think this idea can be applied to many cryptographic algorithms. © 2021, Springer Nature Switzerland AG.

关键词： Computational complexity

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New advanced computing Architecture for Cryptography Design and Analysis by D-Wave Quantum Annealer

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Tsinghua Science and Technology 2022年第4期27卷 751-759页

作者： Xiangmin Ji Baonan Wang Feng Hu Chao Wang Huanguo Zhang College of Computer Information Science Fujian Agriculture and Forestry UniversityFuzhou 350002China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444 State Key Laboratory of Cryptology Beijing 100878China laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai UniversityShanghai 200444 State Key Laboratory of Cryptology Beijing 100878and also with Center for Quantum ComputingPeng Cheng LaboratoryShenzhen 518000China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072

Universal quantum computers are far from achieving practical *** D-Wave quantum computer is initially designed for combinatorial ***,exploring the potential applications of the D-Wave device in the field of cryptography is of great ***,although we optimize the general quantum Hamiltonian on the basis of the structure of the multiplication table(factor up to 1005973),this study attempts to explore the simplification of Hamiltonian derived from the binary structure of the integers to be factored.A simple factorization on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the D-Wave ***,by using the quantum computing cryptography based on the D-Wave 2000 Q system,this research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired Simulated Annealing(QISA)*** functions and a high-performance platform are introduced,and additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be *** comparison between QISA and Quantum Annealing(QA)on six-variable bent functions not only shows the potential speedup of QA,but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a practical cryptography design.

关键词： Quantum Annealing(QA) factorization Boolean functions brain-inspired cognition

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Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization 24

Coarse-to-Fine Proposal Refinement Framework for Audio Tempo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Junyan Lu, Wei Luo, Xiangyang Yang, Rui Wang, Qian Cao, Xiaochun School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Alibaba Group Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen China

ISBN: (纸本)9798400706868

Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio. However, existing countermeasures still serve a classification purpose and fail to perform meaningful analysis of the start and end timestamps of partial forgery segments. To address this challenge, we introduce a novel coarse-to-fine proposal refinement framework (CFPRF) that incorporates a frame-level detection network (FDN) and a proposal refinement network (PRN) for audio temporal forgery detection and localization. Specifically, the FDN aims to mine informative inconsistency cues between real and fake frames to obtain discriminative features that are beneficial for roughly indicating forgery regions. The PRN is responsible for predicting confidence scores and regression offsets to refine the coarse-grained proposals derived from the FDN. To learn robust discriminative features, we devise a difference-aware feature learning (DAFL) module guided by contrastive representation learning to enlarge the sensitive differences between different frames induced by minor manipulations. We further design a boundary-aware feature enhancement (BAFE) module to capture the contextual information of multiple transition boundaries and guide the interaction between boundary information and temporal features via a cross-attention mechanism. Extensive experiments show that our CFPRF achieves state-of-the-art performance on various datasets, including LAV-DF, ASVS2019PS, and HAD. © 2024 ACM.

关键词： Contrastive Learning

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