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Software Defect Classification and Analysis Study

Software Defect Classification and Analysis Study

引用

Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Liyuan Zheng Yang Xie Boyuan Hou Xin Xie Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

This study proposes a set of improved defect categorization criteria and analysis strategies based on a synthesis of a variety of commonly used software defect categorization methods, combined with the actual software development and testing process. By exploring the defect analysis process, this paper discusses the application of this standard in practice. Finally, basic methods and processes are proposed for software defect prevention, providing theoretical and practical guidance for quality management in the software development process.

关键词： Productivity Prevention and mitigation Standards organizations Organizations Reliability theory Software Software reliability

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A Fuzzing Method for Embedded Bus Based on Dynamic Mutation of Message Sequences

A Fuzzing Method for Embedded Bus Based on Dynamic Mutation ...

引用

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

作者： Fengjiao He Chi Hu Wansheng Yang Hao Zhang Fei Deng Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

As an important support for the communication link of embedded systems, the bus protocol faces many security threats while meeting diverse business needs, and how to ensure the security of the bus link of embedded systems has become an important security issue. In order to meet the requirements of generalized security detection of embedded system bus protocols, this paper proposes a fuzzing method for embedded bus protocol based on dynamic mutation of message sequences. Based on the bus protocol specification, we model the protocol message and interaction process, and realize the fuzzing test of the protocol process in combination with the association of messages in the message sequence. At the same time, we combined the fuzzer with a timer to increase the time perception of the method. In order to verify the effectiveness of our method, this paper applies the proposed method to the CAN bus fuzzing of the Internet of Vehicles simulation platform, and successfully finds a security flaw in the process of motor speed data frame processing of the platform. Moreover, we carry out a comparative experiment with several common fuzzing tools, and the experimental results show that in the defect detection of the CAN bus of the Internet of Vehicles simulation platform, our method can increase the automatic mutation support for the interval_time field, and can discover more security defects.

关键词： Protocols Embedded systems Software algorithms Software quality Fuzzing Software reliability Security Vehicle dynamics Internet of Vehicles Defect detection

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Research and Implementation of Log-Based Anomaly Detection Platform Based on ELK+Kafka

Research and Implementation of Log-Based Anomaly Detection P...

引用

International Conference on computer and Communications (ICCC)

作者： Shuai Xu Ziyang Meng Han Wang Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Anomaly detection is vitally essential to modern large-scale distributed systems. Over the years,many log-based anomaly detection methods have been proposed. Logs record the real-time running information of processes in distributed systems and show the running status of the processes. However, when the reliability issues that happen in distributed systems, operators often inspect the logs manually with multi-rule matching and multi-keyword search. Additionally, The increasing scale and complexity of distributed systems, make the volume of logs explode, which renders the infeasibility of manual inspection. To reduce manual effort, we provide a log-based anomaly detection platform, which is based on ELK (Elasticsearch, Logstash, Kibana), Kafka and deep learning-based methods. Our proposed platform is evaluated using two different publicly-available production log datasets, and our results demonstrate that it achieves the $F_{1}$ -score of 0.792 and 0.829, respectively, outperforming the manually inspecting log anomalies.

关键词： Training Learning systems Systems operation Noise Microservice architectures Production Manuals Real-time systems Reliability Anomaly detection

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Robust LiDAR-Camera Alignment over Point Cloud Density Variation

Robust LiDAR-Camera Alignment over Point Cloud Density Varia...

引用

Chinese Automation Congress (CAC)

作者： Angfan Zhu Tao Ma Institute of Computer Application China Academy of Engineering Physics Sichuan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

For the first time, we raise and address the research problem of robust LiDAR-Camera alignment (LCA) over point cloud density variation, which is essentially important to the applications like autonomous driving and robot navigation. Our key idea is to generate the regular point cloud that holds the characteristics of density-insensitivity and evenness jointly for extracting robust and effective LCA feature. Our intuition lies in 2 main folders. That is, density-insensitivity aims to make the generated point cloud have a fixed number of points in local spaces. And, evenness requires that the points in local spaces can be evenly distributed to better recover the scene. The density-insensitivity and evenness ensure the generated point cloud can provide robust and effective information for LCA tasks. Specifi-cally, a 2D CNN model is used to achieve the density-insensitivity and evenness of generated regular point clouds. Additionally, a novel loss function is proposed to train our model without ground truth. The experiments on KITTI dataset verify superiority of our proposition. Our method can achieve comparable performance with state-of-the-arts LCA methods under original testing point clouds. As the point cloud density decreases, the performance of our method gradually outperforms them. When the point cloud density is very sparse, the performance advancement of our method is over 1.5 m / 0.4° on translation / rotation error with mis-alignment magnitudes of up to ±1.5m and ±20°.

关键词： Point cloud compression Translation Automation Accuracy Navigation Feature extraction Robots Autonomous vehicles Testing

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Management of Multiple Microgrids Considering Soft Loads Based on Federated Reinforcement Learning

Management of Multiple Microgrids Considering Soft Loads Bas...

引用

Electrical engineering and Automation (IFEEA), International Forum on

作者： Zixin Mu Lei Tang Chen Zhou Ling Li Yang Long Dong Li Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798331516611

ISBN: (纸本)9798331516628

As the energy transition advances, multi-microgrid systems are being widely applied due to their advantages in effectively accommodating clean energy and achieving zero-carbon power supply within certain areas. However, due to the instability and intermittency of renewable energy units within these systems, intelligent regulation of the output of various units in multi-microgrid systems has become an urgent issue to address. Reinforcement learning (RL), benefiting from its strength in making decisions through interaction with the environment, has emerged as one of the effective solutions to this problem. Yet, the generalization performance of the model places demands on training data and diversity. To this end, a reinforcement learning model based on a federated learning framework is proposed, taking into account soft loads on the user side and power losses within the network. After modeling the multi-microgrid system, the specific training process can be briefly summarized as follows: first, mixed strategies are employed at each local end to train the RL models within individual microgrids; then, parameters required for model updates are transmitted to a global server for federated learning; subsequently, the models of each microgrid are updated based on the global server model. Finally, simulations and analyses are conducted on a small-scale multi-microgrid example composed of three microgrids, verifying the effectiveness and superiority of the algorithm, as well as its enhancement of market economic benefits.

关键词： Training Federated learning Training data Reinforcement learning Microgrids Scheduling Regulation Smart grids Servers Load modeling

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Networked Vehicle Practices Based on Networked Firing Ranges

Networked Vehicle Practices Based on Networked Firing Ranges

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Wenqi Zheng Hao Ren Siqi Yang Zhi Chen Jiajun Xie Wenqiang Pan Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In recent years, with the rapid development of network and computer technologies, the Governments of the United States, Japan, the United Kingdom and the European Union, as well as influential organizations, have successively carried out the construction of cyberspace ranges targeting the verification of new network technologies. New technology validation based on cyberspace ranges can solve the problems of high cost, difficult to manage iterative versions, and difficult to measure the accuracy of results. Based on the above advantages, this paper, in order to solve the problem of autonomous driving of cars in the park, which is difficult to drive safely under the influence of environmental uncertainties, completes the hardware design of intelligent cars in the cyberspace target range and proposes a Bayesian network-based design method of steering redundancy for hardware circuits, which divides the steering data of the two cars under different environmental factors into training set and test set. The Bayesian network parameters are predicted by the training set, and the prediction effect of the Bayesian network is tested by the test set. The prediction results show that the steering accuracy prediction of the smart car under different environmental factors is within the 85% confidence interval of the Bayesian network prediction, which provides a feasible way to design a safe and efficient underlying controller for smart cars.

关键词： Training Accuracy Uncertainty Redundancy Cyberspace Hardware Environmental factors Bayes methods Automobiles Wire

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Whispering Packets: Hiding Messages in VoIP Traffic

Whispering Packets: Hiding Messages in VoIP Traffic

引用

International Conference on computer engineering and applications (ICCEA)

作者： Hao Ren Wenqiang Pan Siqi Yang Wenqi Zheng Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

This paper takes the popular applications WeChat and Enterprise WeChat as examples and investigates the embedding of information in VoIP traffic for data transmission. Exploring the alterations in VoIP traffic characteristics under different vocal environments, this study proposes a covert communication method that transforms the traffic features from silent vocal states to noisy by modifying the length distribution of VoIP traffic. Experimental results demonstrate that this approach achieves channel bandwidths of up to 35kbps and 28kbps in the silent vocal environments of WeChat and Enterprise WeChat, respectively.

关键词： Social networking (online) Bandwidth Transforms Message services Noise measurement Data communication Payloads

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DLD: Defending Against Backdoors Using Differential Logits

DLD: Defending Against Backdoors Using Differential Logits

引用

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Ming Yi Yulong Wang Kai Deng Mingyong Yin Fanzhi Meng Yubing Duan Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

The existing research has shown that deep learning models are vulnerable to backdoor attacks, in which malicious actors can manipulate the model’s predictions by embedding backdoors. As the stealthiness of these backdoor trigger mechanisms increases, traditional methods for detecting backdoors become increasingly inadequate. This paper presents a novel approach for detecting backdoors based on analyzing variations in model logits. Since the primary objective of a backdoor is to alter the model’s prediction results, detecting backdoors through the analysis of logits variations offers a robust method for backdoor determination. To improve the accuracy of identifying backdoor models, our proposed method not only examines the logits variations of the model under investigation but also introduces an auxiliary normal model with a simpler structure. The presence of a backdoor in the model under investigation is determined by contrasting the differences in logits variations between the model under investigation and the normal model. Furthermore, to enhance detection speed, we introduce a dataset reduction mechanism to streamline the testing dataset. Experimental results demonstrate that our method successfully defends against high-stealth backdoor attacks such as WaNet and LIRA. Moreover, in terms of detection speed, our method outperforms mainstream methods like Neural Cleanse, which takes several hours for a single detection, by requiring only several minutes.

关键词： Deep learning Analytical models Automation Accuracy Refining Predictive models Inspection

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A Few-Shot Semantic Segmentation Network with Mixed Prototypes and Base Learner

A Few-Shot Semantic Segmentation Network with Mixed Prototyp...

引用

IEEE Advanced Information technology, Electronic and Automation Control Conference (IAEAC)

作者： Han Wang Ziyang Meng Yakun Zhang Institute of Computer Application China Academy of Engineering Physics Sichuan China

Few-shot semantic segmentation methods aim to segment new object categories with limited labeled samples. Existing approaches struggle to extract comprehensive semantic matching information from target regions and mitigate noise from complex backgrounds. To address background noise, this paper proposes a novel few-shot semantic segmentation approach using prototype generation and base class learning. A pyramid pooling convolutional layer extracts rich prototype vectors by discerning differences between the core mask and label mask. Additionally, a base class learning module captures semantic information from base class objects during training, effectively suppressing interference from background regions and aiding in background semantic matching. Comparative and ablation experiments demonstrate the superior performance of the proposed model. Evaluation on benchmark datasets shows its effectiveness, with results on Pascal-5i dataset surpassing the baseline model, achieving an average intersection over union of 67.15% in one-shot scenarios.

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Research on the Software Scale Evaluation Method of "Intelligent and Expert Collaboration"

Research on the Software Scale Evaluation Method of "Intelli...

引用

computer technology and Information Science (ISCTIS), International Symposium on

作者： Sen Sun Yizheng Tao China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In software engineering practice, accurate and efficient system scale evaluation is crucial for ensuring the accuracy of project management and fund allocation. Traditional software system scale assessment methods, such as expert decision-making, COCOMO models, functional point analysis, and analogy methods, are widely used, but are limited by high manual dependencies, low efficiency, and subjectivity. Therefore, this study proposes an innovative collaborative evaluation method - DALSE (Domain Adaptation Learning Size Evaluation) evaluation method, which combines automation models with expert experience. This method is based on the IFPUG function point standard, constructs the BiLSTM-CRF model software for automatic recognition of function point types, and introduces domain adaptive learning technology to effectively alleviate the problem of data scarcity in specific fields, achieving flexibility and accuracy in cross domain applications. Although the DALSE automation model has shown excellent performance in experiments, considering the complexity and importance of software scale evaluation, this study advocates an "Intelligent+Expert" collaborative evaluation model, which combines expert experience to verify the results and multi expert collaborative decision-making to determine the complexity factor of the project. The initial evaluation results are adjusted to ensure the comprehensiveness and reliability of the evaluation. This collaborative work mode effectively integrates automation technology while retaining manual verification to increase evaluation credibility, greatly improving evaluation efficiency and quality. By complementing intelligence and manual labor, DALSE provides a more reliable and efficient solution for software scale assessment, especially suitable for capital intensive projects, ensuring the scientific and rational cost estimation, and strongly supporting modern decision-making in project management.

关键词： Adaptation models Accuracy Automation Decision making Collaboration Project management Manuals Software Complexity theory Standards

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