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检索条件"机构=School of Reliability Engineering and Systems Engineering"

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Bit Attacking Deep Neural Networks Based on Complex Networks Theory 24

Bit Attacking Deep Neural Networks Based on Complex Networks...

引用

24th IEEE International Conference on Software Quality, reliability and Security Companion, QRS-C 2024

作者： Zhou, Yijing Yang, Shunkun Shao, Qi Zhang, Yuhao School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798350365658

With the widespread application of deep learning technology in safety-critical systems, the issues of security and robustness are increasingly prominent, becoming an important topic that cannot be ignored. In addition to input-side threats represented by adversarial examples, model-side attacks such as poisoning attacks or bit flipping have also become fatal security threats in deep learning systems. Despite existing research in bit-flip attack being able to achieve significant attack effects by flipping a small number of bits, there still lacks effective bit attack strategies in dealing with limitations such as the lack of specific use cases, inability to perform forward propagation, and the presence of gradient masking. Therefore, this paper proposes a bit attack strategy based on complex network theory for fully connected neural networks (FCNNs). This strategy relies solely on the structural information of the model, abstracting the neural network into a directed weighted graph, and creating graph structural metrics of the neural network. Utilizing these metrics to identify vulnerable neurons and weights, it guides the execution of bit attacks. Experimental results demonstrate that bit attacks guided by complex network metrics are significantly superior to random attacks, and attacks guided by Link Weights metrics require only up to 50 bits at most to increase the neural network error by 80%. © 2024 IEEE.

关键词： Deep neural networks

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Automated Structural Test Case Generation for Human-Computer Interaction Software Based on Large Language Model 11

Automated Structural Test Case Generation for Human-Computer...

引用

11th International Conference on Dependable systems and Their Applications, DSA 2024

作者： Kang, Long Ai, Jun Lu, Minyan Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (纸本)9798331532390

As software systems expand in complexity, managing the vast and varied collection of test cases becomes increasingly difficult with traditional manual testing methods. This paper presents a new approach for automating the generation of structured test cases, named Test Element Extraction and Restructuring (TEER), which leverages the advanced natural language processing capabilities of large language models (LLMs). Specifically targeting human-computer interaction (HCI) software, TEER employs prompt tuning techniques to extract critical elements from natural language test cases and systematically reassemble them into structured formats. The study evaluates the effectiveness of TEER by applying it to common test cases from desktop HCI applications. The experimental results demonstrate that this method successfully produces structured test cases that meet predefined requirements. © 2024 IEEE.

关键词： Software testing

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Semantic-Weighted Word Error Rate Based on BERT for Evaluating Automatic Speech Recognition Models 11

Semantic-Weighted Word Error Rate Based on BERT for Evaluati...

引用

11th International Conference on Dependable systems and Their Applications, DSA 2024

作者： Zhang, Yutao Ai, Jun Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (纸本)9798331532390

To address the limitations of traditional evaluation metrics, which fail to differentiate the importance of words and cannot provide detailed, accurate assessments of Automatic Speech Recognition (ASR) models, this paper introduces the Semantic-Weighted Word Error Rate (SWWER). SWWER leverages the BERT model to assign weights to each word based on its contribution to the semantic content of the text, enabling a more accurate and nuanced evaluation of ASR models. Additionally, to provide a more comprehensive assessment of ASR models from both lexical and semantic perspectives, this study proposes a hybrid evaluation metric, H-eval, defined as the harmonic mean of SWWER and Semantic Similarity (SD). Furthermore, this paper explores the potential of integrating Large Language Models (LLMs) in correcting ASR transcriptions. Notably, SWWER effectively reflects the impact of LLM corrections on the transcribed text, indicating that for the same number of erroneous words, higher weights assigned to these errors correlate with greater semantic loss, potentially implying less effective corrections by the LLM. The SWWER and H-eval merics proposed in this paper not only offer precise and comprehensive evaluations but also provide new insights for improving ASR systems and optimizing their performance through the use of LLMs. © 2024 IEEE.

关键词： Semantics

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Complex Network Theory-based Deep Neural Network Degradation Analysis in the Context of Bit Attack 24

Complex Network Theory-based Deep Neural Network Degradation...

引用

24th IEEE International Conference on Software Quality, reliability and Security Companion, QRS-C 2024

作者： Shao, Qi Yang, Shunkun Zhou, Yijing Lu, Dan Yao, Qi School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798350365658

The reliability of deep learning technologies has become an unavoidable issue due to their application in safety-critical domains. In addition to the well-known adversarial examples, bit attacks hijack the execution of functions and pose threats to the models. Therefore, we propose a degradation analysis framework leveraging complex network theory for the first time to comprehend the impact of bit attacks on deep neural networks. Within the framework, a bit attack simulator and a structural-functional (S-F) network analyzer are developed. The bit attack simulator obtains erroneous models through training, quantization, and bit flipping. And the structural parameters and functional information of the attacked model are modeled and dissected within the S-F network analyzer. Bit attack results indicate that the attack thresholds for fully connected networks are similar, whereas convolutional neural networks can vary by up to 100-fold. S-F network experimental results reveal that bit attacks disrupt the trained narrow distribution of structural parameters, forcing them towards uniformly, and the performance degradation is caused by reducing the global efficiency and clustering coefficient of functional information. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Design for Testability and Evaluation System Based on systems Modeling Language 15

Research on Design for Testability and Evaluation System Bas...

引用

15th IEEE Global reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Shi, Junyou Chen, Wenlong Zhou, Huidong School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798350354010

In modern complex system design, design for testability is a critical aspect to ensure system reliability, maintainability, and safety. Traditional methods of design for testability can no longer meet the complex demands of new generation equipment and systems. This paper explores testability design techniques based on SysML (systems Modeling Language) and analyzes their specific applications in the testability design and evaluation system. By thoroughly analyzing system functional requirements and combining SysML modeling methods, a new design approach is proposed to enhance the clarity and traceability of testability requirements, optimizing the design of diagnostic schemes, condition monitoring, and fault detection. Through case studies, this paper demonstrates how to use self-developed testability software to conduct testability analysis and evaluation at different stages of system design, ensuring that the design meets predetermined testability requirements, and showing the effectiveness and feasibility of this method in practical applications. © 2024 IEEE.

关键词： Fault detection

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Performance Degradation Characteristics Extraction of Lithium-ion Batteries based on Convolutional Neural Networks 15

Performance Degradation Characteristics Extraction of Lithiu...

引用

15th IEEE Global reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Yao, Jinyong Liang, Jinming Gao, Zhanfei Zeng, Sizhe School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798350354010

Currently, lithium-ion batteries, with their high energy density and memoryless effect, are comprehensively applied to the fields of aerospace, new energy vehicles, and mobile terminal devices. However, the performance degradation caused by capacity loss will weaken the efficiency of lithium-ion batteries, and even cause property damage and casualties in serious cases. Battery capacity is a physical quantity that reflects the remaining useful life of the battery, but collecting the capacity data of battery directly is costly and has a long cycle. Therefore, this paper proposes a Performance Degradation Characteristics (PDCs) extraction method to find PDCs that can indirectly and effectively reflect the decline of battery capacity. First, a characteristic extraction method based on Convolutional Neural Network (CNN) is proposed to extract PDCs from original battery data, which can automatically learn the characteristic representation, and possesses high extraction efficiency and strong generalization ability. Second, Nine PDCs, including seven Measurable Characteristics (MCs) and two Computational Characteristics (CCs), are extracted to describe the battery capacity decline validly. Finally, The feasibility and effectiveness of the extraction method are verified by using the nine PDCs to predict the remaining using life of lithium-ion batteries based on a series of CNN models. © 2024 IEEE.

关键词： Convolutional neural networks

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A Mission reliability Optimization Model for Safety-Critical Equipment Considering both Internal Degradation and External Shocks 15

A Mission Reliability Optimization Model for Safety-Critical...

引用

15th IEEE Global reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Kang, Zijian Wei, Fanping Ma, Xiaobing Yang, Li School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798350354010

Safety-critical equipment such as Unmanned Aerial Vehicles (UAVs), submarines, fleets, and military aircraft must perform a variety of missions where mission reliability and equipment survivability are paramount. To enhance survivability, mission can be aborted upon encountering certain failure conditions. In this study, a novel mission abort policy is devised that allows for risk control of both internal degradation and external shocks. The cumulative impact of shocks on system degradation is quantified via a generic stochastic model. Inspections are executed equidistantly to reveal the underlying degradation damage, following which adaptive abort decisions are made using a control limit policy. By jointly optimizing the inspection interval and abort threshold, the long-term operational cost of the system is minimized. Numerical experiments exemplify the model effectiveness in mission risk control and loss mitigation. © 2024 IEEE.

关键词： Stochastic systems

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Prediction of Remaining Useful Life based on Gramian Angular Field and Recurrent Neural Network 6

Prediction of Remaining Useful Life based on Gramian Angular...

引用

6th International Conference on System reliability and Safety engineering, SRSE 2024

作者： Zhang, Wenduo Wang, Han Ma, Xiaobing Ji, Ziguang Ye, Kewei Chen, Qian Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (纸本)9798350356083

Predicting the Remaining Useful Life (RUL) using time-series data obtained from sensors can significantly enhance the reliability and safety of equipment. This is crucial for the health management tasks of in-service equipment. To fully extract the temporal and spatial features from sensor data, this paper proposes a RUL prediction method that integrates Gramian Angular Summation Fields (GASF), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Specifically, the sensor data is first converted into image data with temporal correlation through GASF. Subsequently, CNN is utilized for feature extraction from the image data. Based on this, an RNN model is established to predict the RUL by leveraging spatiotemporal characteristics. The superiority of the proposed RUL prediction method is finally validated using a turbofan engine dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Inverse Moment Estimation of Three-parameter Power Function Distribution with Extension to Accelerated Life Test 6

Inverse Moment Estimation of Three-parameter Power Function ...

引用

6th International Conference on System reliability and Safety engineering, SRSE 2024

作者： Liu, Yujie Wang, Han Ma, Xiaobing Ji, Haodi Zhang, Runyu Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (纸本)9798350356083

Power function distribution is used frequently in both reliability and life assessment. However, in some cases, the traditional method fails in parameter estimation of the three-parameter power function distribution. We propose a novel method to resolve this difficulty. This method utilizes the constant moment of the population while considering the bias-corrected estimator of the location parameter and scale parameter. Further, the proposed method is extended to accelerated life tests. The validity of life assessment is ensured by deriving the necessary condition for mechanism equivalence, and the effectiveness of the proposed method is proven using simulations. Finally, a real-world example is presented to illustrate the performance of the method. © 2024 IEEE.

关键词： Power distribution reliability

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Research on Joint reliability Test Methods for Software-Intensive Avionics Products Based on Scenario Models 15

Research on Joint Reliability Test Methods for Software-Inte...

引用

15th IEEE Global reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Yao, Jinyong Nie, Yuhui Wang, Xiangfen Gao, Zhanfei School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798350354010

Software-intensive systems play a critical role in complex airborne products, where the system-level failure mechanisms are closely related to the interaction between software and hardware. These failures not only impact the reliability and safety of the system but also the integrity of its functionality. These systems typically have high functional requirements and complex failure mechanisms, with physical redundancy in resources. Therefore, thorough validation during the design phase is crucial to ensure comprehensive and sufficient testing. Standards such as DO178 and DO254 provide guidance in the reliability validation of such systems. However, existing research shows that the scenarios used in software-hardware joint reliability test designs do not sufficiently cover all possible operational conditions and environments. This shortfall may lead to undetected software-hardware integration issues, compromising the comprehensiveness and accuracy of reliability assessments. Moreover, there is a lack of research on software-intensive systems. This paper conducts an in-depth analysis of scenario models, integrating the design and development characteristics of commercial aircraft avionics systems, and focusing on the mission scenarios and capability requirements of software-hardware intensive systems. It proposes a novel method for constructing software-hardware joint reliability test profiles based on scenario models. This approach comprehensively captures the requirements for reliability integration testing, conducting system-level and component-level reliability integration testing based on model-based techniques, and serving as a primary basis for reliability validation. © 2024 IEEE.

关键词： Integration testing

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