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19th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Long, Dan Wang, Juan Han, Bin Chen, Ding Shi, Lei Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

ISBN: (纸本)9798350304398

With the national Belt and Road strategy in place, meteorological operational systems will face domestic and international dangers, and it is very urgent and important to protect the safety of meteorological equipment and resources. The existing meteorological operational system uses a role-based access control model for permission management, whose permissions do not change during one authorized access, and cannot sense the threat of user behavior to the system in real time, much less adjust user permissions in time, thus failing to cope with the security needs of the weather business system. In this paper, we propose a Meteorological Operational system-oriented Usage Control (MS-UCON) model to address this problem. The model refines the UCON model according to industry characteristics and business needs, and adds a periodic trust assessment module and a collection of trust value assessment factors to monitor user behavior in real time, perform periodic trust assessment on user behavior, sense user risky behavior in time, dynamically update user trust value attributes, and dynamically adjust permissions. Finally, the security of the MS-UCON model is proved based on state machine theory. The results show that the MS-UCON model can solve the problem that user privileges cannot be dynamically adjusted in meteorological operational systems, and also meet the security requirements of meteorological operational systems. © 2023 IEEE.

关键词： Access control

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Side-Channel Analysis and Countermeasures of Domestic Block Cipher uBlock 11

Side-Channel Analysis and Countermeasures of Domestic Block ...

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Shang, Xinyan Liang, Kaifeng Wei, Congming Ding, Yaoling Sun, Shaofei Wang, An Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Hangzhou Normal University Key Laboratory of Cryptography of Zhejiang Province Hangzhou China Key Laboratory of Sichuan Province Advanced Cryptography and System Security Sichuan China

ISBN: (纸本)9798350314014

Nowadays, IoT devices are ubiquitous. Addressing data security issues in resource-limited devices is a key focus of IoT security. Therefore, lightweight cryptographic algorithms and their security research have received increasing attention. The uBlock is a lightweight block cipher proposed in 2019, which has advantages in software performance and hardware implementation, such as efficiency and low energy consumption. Research shows that the uBlock can effectively resist traditional analysis methods such as integral analysis and differential analysis. However, there is currently a lack of related research showing that the uBlock can effectively resist side-channel analysis. In this paper, we successfully complete the correlation power analysis on the uBlock and give specific experimental results. Subsequently, we propose a masking countermeasure for the uBlock software implementation and prove its security against correlation power analysis. Its implementation shows that the protected uBlock has increased code size, time, and RAM consumption by 37.73%, 38.31%, and 20.80%, respectively, compared to the unprotected one. © 2023 IEEE.

关键词： Energy utilization

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An Industrial Control system Risk Assessment Method 20

An Industrial Control System Risk Assessment Method

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Jian, Shu Qiuxia, Xie Bo, Xian Chenli, Wang Jiazhong, Lu Yuanyuan, Huang School of Cybersecurity Chengdu University of Information Technology Sichuan Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan Chengdu610225 China

ISBN: (纸本)9798350318982

With the rapid development of information technology, the combination of industrial control system and network communication technology is widely used in energy, manufacturing, military, aerospace and transportation. Attacks against industrial control system every year are lower than those against the Internet, but they have a more significant impact on society. Therefore, risk assessment of industrial control system is the first task to prevent attacks. In this paper, we propose a risk assessment method for industrial control system, starting from the idea of game theory, combining the assessment index assignment methods of subjective hierarchy process analytic hierarchy process (AHP) and objective entropy weight method (EWM), determining the target matrix according to the weights of the two, and utilizing the approximation of the ideal solution. The final evaluation results are obtained by using the technique for order preference by similarity to ideal solution (TOPSIS). The results show that the combination of subjective and objective methods can make the weighting of assessment indicators more reasonable and effective. © 2023 IEEE.

关键词： Risk assessment

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Hybrid Classical Quantum Neural Network with High Adversarial Robustness

Hybrid Classical Quantum Neural Network with High Adversaria...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Yang, Yongxi Zhang, Shibin Yan, Lili Chang, Yan Chengdu University of Information Technology Chendu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chendu610225 China SUGON Industrial Control and Security Center Chendu610225 China

As the realms of quantum computing and machine learning converge, a novel domain, termed quantum machine learning, is progressively forming within the sphere of artificial intelligence studies. Nonetheless, akin to its classical counterpart, this emerging field is not exempt from security vulnerabilities. Quantum machine learning systems, regardless of whether they process classical or quantum inputs, are susceptible to minor perturbations that can erroneously skew classification outcomes. These minute disruptions, often imperceptible to human observation, present a significant challenge in ensuring the integrity of quantum classifiers. As the complexity of quantum classifiers increases, their vulnerability also gradually grows. To mitigate this issue, this paper proposes a novel hybrid classical-quantum neural network model that enhances the model's adversarial robustness by adding a preprocessing layer for noise reduction and data reconstruction. Experiments demonstrate that this model exhibits higher efficiency and accuracy in noisy environments and against adversarial attacks. © 2024 Y. Yang, S. Zhang, L. Yan & Y. Chang.

关键词： adversarial robustness preprocessing layer Quantum neural network

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Quantum Fuzzy Support Vector Machine for Binary Classification

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Computer systems Science & Engineering 2023年第6期45卷 2783-2794页

作者： Xi Huang Shibin Zhang Chen Lin Jinyue Xia School of Cybersecurity Chengdu University of Information TechnologyChengdu610225China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu610225China International Business Machines Corporation(IBM) New York14201USA

In the objective world,how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine *** support vector machine(FSVM)not only deals with the classifi-cation problems for training samples with fuzzy information,but also assigns a fuzzy membership degree to each training sample,allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin,reducing the effect of outliers and noise,Quantum computing has super parallel computing capabilities and holds the pro-mise of faster algorithmic processing of ***,FSVM and quantum com-puting are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate *** paper research and propose an efficient and accurate quantum fuzzy support vector machine(QFSVM)algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty *** central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations(HHL algorithm)and the least-squares method to solve the quadratic programming problem in the *** proposed algorithm can deter-mine whether a sample belongs to the positive or negative class while also achiev-ing a good generalization ***,this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers,and achieve accurate classification of handwritten *** compared to FSVM,QFSVM’s computational complexity decreases expo-nentially with the number of training samples.

关键词： Quantum fuzzy support vector machine(QFSVM) fuzzy support vector machine(FSVM) quantum computing

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Differential Privacy Face Image Publishing Method Based on Neighboring Pixels Merging 21

Differential Privacy Face Image Publishing Method Based on N...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Lang, Ming Yongli, Wang Yuanyuan, Huang Junyu, Chen School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Dazhou Key Laboratory of Government Data Security Dazhou635000 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (纸本)9798331519254

Face images contain rich and sensitive personal information. Direct release of facial images on the Internet may lead to the disclosure of personal information. In order to protect the privacy information in facial images, this paper proposes a PMP method based on neighboring pixels merging, which meets the requirements of differential privacy. First, the gray scale matrix of the face image is leveled into a one-dimensional data stream and the position index of each pixel is recorded while traverse through the pixels. Given the large number of duplicate pixels in the face image, duplicate pixels as well as neighboring pixels were grouped to reduce the sensitivity of differential privacy, and noise was subsequently added to each group. Eventually, each pixel is replaced to its original position according to the index, and the new one-dimensional data stream is converted back to the original matrix structure. The experiment based on real face image data set used the combination of support vector machine (SVM) and principal component analysis (PCA). The results show that the PMP algorithm is 30.57% higher than the LAP algorithm in terms of the average accuracy of face recognition, and the proposed PMP algorithm has higher usability and robustness. © 2024 IEEE.

关键词： Differential privacy

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An Embedding-Based Multi-Modal Few-Shot Learning Method for Network Intrusion Detection 21

An Embedding-Based Multi-Modal Few-Shot Learning Method for ...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Qiulin, He Jinquan, Zhang Yuanyuan, Huang School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Dazhou Key Laboratory Government Data Security Dazhou635000 China

ISBN: (纸本)9798331519254

In this paper, we introduce an embedding-based few-shot learning framework, aiming to achieve classification by calculating the similarity measure between unknown samples and known samples in the embedding space. This framework mainly consists of two parts: embedding space and similarity calculation stage. In the embedding space, semantic information and visual features are embedded into a common space to achieve multi-modal embedding. In the similarity calculation stage, the classification task is achieved by calculating multiple score indicators between vectors. The experimental results show that when the number of support sets is 5, the accuracy on the CICIDS2017 and KDDCUP99 datasets reaches 98.89% and 99.85%, demonstrating excellent performance in the domain of few-shot learning for intrusion detection. © 2024 IEEE.

关键词： Network intrusion

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Low-overhead fault-tolerant error correction scheme based on quantum stabilizer codes

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Chinese Physics B 2022年第4期31卷 143-150页

作者： Xiu-Bo Chen Li-Yun Zhao Gang Xu Xing-Bo Pan Si-Yi Chen Zhen-Wen Cheng Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information Science and Technology North China University of TechnologyBeijing 100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu 610025China

Fault-tolerant error-correction(FTEC)circuit is the foundation for achieving reliable quantum computation and remote ***,designing a fault-tolerant error correction scheme with a solid error-correction ability and low overhead remains a significant *** this paper,a low-overhead fault-tolerant error correction scheme is proposed for quantum communication ***,syndrome ancillas are prepared into Bell states to detect errors caused by channel *** propose a detection approach that reduces the propagation path of quantum gate fault and reduces the circuit depth by splitting the stabilizer generator into X-type and ***,a syndrome extraction circuit is equipped with two flag qubits to detect quantum gate faults,which may also introduce errors into the code block during the error detection ***,analytical results are provided to demonstrate the fault-tolerant performance of the proposed FTEC scheme with the lower overhead of the ancillary qubits and circuit depth.

关键词： fault-tolerant error correction quantum stabilizer code gate fault quantum circuit

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An Enhanced Fusion Model for Android Malware Detection Leveraging Multi-Code Fragment Features and Fuzzy Hashing 20

An Enhanced Fusion Model for Android Malware Detection Lever...

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Lingrong, Han Shibin, Zhang Jiazhong, Lu Yuanyuan, Huang Zhi, Qin School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (纸本)9798350318982

Traditional methods for detecting Android malware require large-scale datasets and complex feature engineering, which are unable to adapt to complex malicious features. Additionally, feature engineering based on fuzzy hash algorithms is widely used but susceptible to bypass attacks by adversaries. To address these issues, this paper proposes an Android malware detection model that multiple Android code snippet features with fuzzy hash features. Simulation experiments demonstrate that using fused fuzzy hash features improves the accuracy of Android malware detection by 7.03%, precision by 6.23%, recall by 13.78%, and F1 score by 9.88% compared to using single fuzzy hash features. The proposed detection method not only focuses on partial features of function call sequences and behaviors of malicious applications but also considers the correlation between call sequences and behaviors, enabling it to adapt to more complex Android malware detection scenarios. © 2023 IEEE.

关键词： Android malware

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Spiderweb Protector: Biomimetic Defense Against Targeted Attacks on Graph Neural Networks 21

Spiderweb Protector: Biomimetic Defense Against Targeted Att...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Zhou, Zeyu Huang, Yuanyuan Wang, Yongli Li, Siyu Lu, Jiazhong He, Peilin Chengdu University of Information Technology Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Dazhou Key Laboratory Government Data Security Dazhou635000 China University of Pittsburgh Department of Informatics and Networked Systems Pittsburgh15260 United States

ISBN: (纸本)9798331519254

Graph Neural Networks (GNNs) have emerged as powerful tools across diverse applications, including social networks, bioinformatics, and recommendation systems. However, their susceptibility to adversarial attacks, particularly targeted attacks, poses a significant challenge. Targeted attacks exploit the message-passing mechanism of GNNs to induce severe performance degradation, even with minimal perturbations. Inspired by the self-repair and adaptive behaviors of spider webs, this paper introduces SpiderWeb Protector, a biomimetic defense strategy designed to enhance GNN robustness. The proposed method integrates three core components: Community-Centric Graph Update, which selectively reinforces key graph structures;Topology Feature Merging, which improves feature representation by incorporating structural insights;and Edge Pruning, which dynamically removes adversarial connections. Comprehensive experiments on three datasets (Cora, Citeseer and Cora_ml) and two GNN models (GCN and GIN) demonstrate the superior performance of SpiderWeb Protector against both direct and influence-targeted attacks, achieving state-of-the-art results. © 2024 IEEE.

关键词： Graph neural networks

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