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3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Han, Xianqun Zhang, Jianbiao Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China

ISBN: (纸本)9781510680449

trusted computing is an effective means of system security protection, encompassing various security solutions. However, these solutions exhibit individual shortcomings, such as the co-location of protective code and system, a singular metric determination standard, and discontinuity in trusted measurements. Consequently, they fail to meet security requirements. Hence, we propose a Behavior-Based Dynamic trusted Measurement scheme (BBDTM). Firstly, the scheme designs a dynamic measurement framework based on a dual-body architecture, segregating protective code from the operating system code to overcome the reliance of protective code on the host system. Subsequently, we assign corresponding weights to behaviors based on their impact on the system using information entropy theory, rendering the metric determination standards more rational. Next, a time window mechanism is designed to achieve a comprehensive trusted measurement of system behavior, addressing issues of trust decay and discontinuity in metric behavior. Finally, relevant experiments demonstrate that our scheme can achieve superior protective results. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： trusted computing

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IOWA-Based Dynamic trusted Assessment Model for System Operational State Behavior 11th

IOWA-Based Dynamic Trusted Assessment Model for System Opera...

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11th International Conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Huang, Haoxiang Zhang, Jianbiao Liu, Yanhui Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China

ISBN: (纸本)9789819912599

In the era of "digital intelligence", the security of critical information systems is vitally important. We propose a trusted assessment model to guarantee the trusted of the system operation process, which is a three-dimensional trustworthiness assessment model. The model comprehensively considered entity trustworthiness, access control compliance trustworthiness, and the trusted of atomic behavior. Subsequently, the Induced Ordered Weighted Averaging (IOWA) is introduced to improve the accuracy and flexibility of the weight setting of the different metric values. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information systems

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Fine-Grained Zero-Shot Object Detection via Semantic Decoupling 24

Fine-Grained Zero-Shot Object Detection via Semantic Decoupl...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Duan, Lijuan Zhang, Zichen Gong, Zhi Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing China

ISBN: (纸本)9798400718182

Recent deep learning models have advanced object detection capabilities, but their reliance on labeled data limits scalability. Fine-grained zero-shot detection remains challenging due to the need to generalize to unseen classes and distinguish subtle features. To address this, we propose Fine-Grained Zero-Shot Object Detection via Semantic Decoupling (ZSD-SD). Our approach introduces a semantic decoupling module to separate visual features into semantic-related and semantic-unrelated components, facilitating alignment between visual and semantic spaces. A semantic-visual learning module further decouples these features, enhancing visual representations relevant to external semantic knowledge. This semantic-related representation significantly improves zero-shot performance. Experiments on two fine-grained object detection datasets demonstrate that ZSD-SD achieves state-of-the-art results. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Blockchain-Driven Revocable Ciphertext-Policy Attribute-Based Encryption for Public Cloud Data Sharing 11th

Blockchain-Driven Revocable Ciphertext-Policy Attribute-Base...

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11th International Conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Zhang, Zhaoqian Zhang, Jianbiao Kang, Shuang Li, Zheng Faculty of Information Technology Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819912599

Sharing data through the public cloud has turned into a popular way to exploit the data value today, and ciphertext-policy attribute-based encryption (CP-ABE) can protect data confidentiality while improving sharing efficiency. In CP-ABE, attribute change raises the issue of revocation, which is solved by a third party in the existing schemes. However, it cannot achieve timely and certain revocation. Thus, this paper proposes a blockchain-driven revocable CP-ABE scheme. We design a re-writable blockchain to realize attribute revocation, and the ciphertext no longer needs to be updated. Attribute authority only needs to update an update factor bound to the attribute and the private keys for non-revoked users, while the revoked users cannot recover the outdated ciphertext. Moreover, our scheme is selectively secure and collusion resistant. Compared with other schemes, the performance of our scheme is satisfactory. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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Analysis and Research on Dynamic Measurements Technology 11th

Analysis and Research on Dynamic Measurements Technology

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11th International Conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Fu, Yingfang Huang, Haoxiang Zhang, Jianbiao Trusted Native Technology Department of Ant Group Beijing Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China

ISBN: (纸本)9789819923618

In cloud computing environments characterized by "dynamics", the traditional static measurement to ensure the trust of the platform system is difficult to guarantee the trust of four dimensions in the cloud computing environment, such as the platform system, computation, transmission, and storage. Dynamic measurement, as an important key technology to secure reliable system operation, has received much attention from academia and industry. This paper introduces the principles and characteristics of dynamic measurement and the common attack risks in the running system. Meanwhile, this paper surveys the state of the art of dynamic measurement schemes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： trusted computing

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JSVulExplorer：A JavaScript Vulnerability Detection Model Based on Transfer Learning 5

JSVulExplorer：A JavaScript Vulnerability Detection Model Ba...

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Chen, Shengquan Jiang, Nan Wu, Zheng Wang, Zichen Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China

ISBN: (纸本)9781510662520

Software vulnerabilities will make the system vulnerable to attack, affect the reliability of the software and cause information leakage, which will have a huge impact on enterprises or individuals. Vulnerabilities are inevitable in software development engineering. Therefore, relying on some methods or tools for continuous vulnerability analysis of code is the solution to minimize software vulnerabilities. We propose a neural network model - JSVulExplorer for static vulnerability analysis of the dynamic programming language JavaScript. The JSVulExplorer focuses on feature enhancement of data. We use pre-training to learn the semantic similarity between code slices, utilize abstract syntax trees to generate path information, and design positional encoding to use the path information. Based on transfer learning, we combine the pre-trained model with path information to improve vulnerability detection performance. Experiments show that JSVulExplorer has significantly improved precision and recall compared to previous models. It is verified that the dynamic event-based programming language can also use the static analysis method for vulnerability detection. © 2023 SPIE.

关键词： Software design

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Quantum Hash Function of Continuous-time Quantum Walk based on Cycle under Broken-line Decoherent Noise

Quantum Hash Function of Continuous-time Quantum Walk based ...

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2023 International Conference on Computer Network Security and Software Engineering, CNSSE 2023

作者： Shi, Wei-Min Tian, Pan Zhou, Yi-Hua Yang, Yu-Guang College of Computer Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

The existing Hash functions based on quantum walk construction are all proposed under the ideal closed environment. In order to better conform to the actual situation and improve the relevant performance of Hash functions, we propose a quantum hash function constructed by continuous quantum walk on the basis of broken-line noise model. We first propose a continuous-time quantum walk model on cycle under the influence of broken-line decoherence, after that apply this model to the construction process of the quantum hash function, and put forward a building method of the hash function on the basis of the broken-line decoherent continuous-time quantum walk on the one-dimensional cycle. The safety analysis and numerical simulation indicate that our hash function has greater anti-collision performance than the existing hash function based on the quantum walk. © 2023 SPIE.

关键词： Hash functions

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Adaptive Early Warning Method of Cascading Failures Caused by Coordinated Cyber-Attacks

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 406-423页

作者： Yufei Wang June Li Jian Qiu Yangrong Chen Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan Hubei430072China China Electric Power Research Institute Beijing102209China Electric Power Planning&Engineering Institute Beijing 100120China

In order to accurately receive early warning of the cascading failures caused by coordinated cyber-attacks(CFCC)in grid cyber-physical systems(GCPS),an adaptive early warning method of CFCC is ***,the evolutionary mechanism of CFCC is analyzed from the attackers'view,the CFCC mathematical model is established,and the transition processes of GCPS running states under the influence of CFCC staged failures are ***,the mathematical model of the adaptive early warning method is ***,the mathematical model of the adaptive early warning method is mapped as an adaptive control process with tolerating staged failures damage,and the solving process is presented to infer the CFCC and its next evolution trend.A decision-making idea for the optimal active defense scheme is proposed considering the costs and gains of various defense ***,to verify the effectiveness of the adaptive early warning method,the warning and defense processes of a typical CFCC are simulated in a GCPS experimental system based on CEPRI-36 BUS.

关键词： Adaptive control theory cascading failures coordinated cyber-attacks early warning grid cyber-physical systems

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Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features

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Journal of Ambient Intelligence and Humanized computing 2023年第11期14卷 15557-15572页

作者： Cui, Song Duan, Lijuan Qiao, Yuanhua Xiao, Ying Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China College of Applied Sciences Beijing University of Technology Beijing100124 China

Epileptic seizure prediction has the potential to promote epilepsy care and treatment. However, the seizure prediction accuracy does not satisfy the application requirements. In this paper, a novel framework for seizure prediction is proposed by learning synchronization patterns. For better representation, bag-of-wave (BoWav) feature extraction is proposed for modeling synchronization pattern of electroencephalogram (EEG) signal. An interictal codebook and preictal codebook, representing the local segments, are constructed by a clustering algorithm. Within a period of EEG signal on all electrodes, local segments are projected onto the learned codebooks. The proposed feature expresses the synchronization pattern of EEG signal with the histogram feature. Moreover, extreme learning machine (ELM) is used to classify the sequence of features. Experiments are performed on the Kaggle seizure prediction challenge dataset and the CHB-MIT dataset. The experiment on the CHB-MIT achieves a sensitivity of 88.24% and a false prediction rate per hour of 0.25. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Epileptic Spike Detection Based on Deep Learning 23

Epileptic Spike Detection Based on Deep Learning

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9th International Conference on computing and Artificial Intelligence, ICCAI 2023

作者： Duan, Lijuan Cui, Yuanqi Wang, Yao Wang, Zeyu Cao, Min Qiao, Yuanhua Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China College of Applied Science Beijing University of Technology University Beijing China

ISBN: (纸本)9781450399029

Epilepsy is a chronic disease with high prevalence and high disease burden. Electroencephalogram (EEG) is one of the most important tools for diagnosing epilepsy. Detection of epileptic spikes from EEG can effectively aid the diagnosis of epilepsy but it is also a time-consuming and laborious process for neurologists, which can only be addressed by automatic spike detection methods. However, existing approaches are limited by the simplistic nature of conventional machine learning algorithms. The work researched about how human experts identify spikes and over-parameterized the process into a deep neural network named temporal-frequential and multi-order difference fusion (TeFreDiA) model which can accurately detect epileptic *** model was evaluated on data collected from 7 subjects with ten-fold cross-validation. Given an average accuracy and sensitivity of 95.64% and 96.89%, the model can be utilized to aid the diagnosis of epilepsy. © 2023 Copyright held by the owner/author(s).

关键词： Electroencephalography

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