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  • Open Access

    ARTICLE

    RESTlogic: Detecting Logic Vulnerabilities in Cloud REST APIs

    Ziqi Wang*, Weihan Tian, Baojiang Cui

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1797-1820, 2024, DOI:10.32604/cmc.2023.047051

    Abstract The API used to access cloud services typically follows the Representational State Transfer (REST) architecture style. RESTful architecture, as a commonly used Application Programming Interface (API) architecture paradigm, not only brings convenience to platforms and tenants, but also brings logical security challenges. Security issues such as quota bypass and privilege escalation are closely related to the design and implementation of API logic. Traditional code level testing methods are difficult to construct a testing model for API logic and test samples for in-depth testing of API logic, making it difficult to detect such logical vulnerabilities. We propose RESTlogic for this purpose.… More >

  • Open Access

    ARTICLE

    A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

    Lanyao Zhang1, Shichao Kan2, Yigang Cen3, Xiaoling Chen1, Linna Zhang1,*, Yansen Huang4,5

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1631-1648, 2024, DOI:10.32604/cmc.2024.046924

    Abstract Unsupervised methods based on density representation have shown their abilities in anomaly detection, but detection performance still needs to be improved. Specifically, approaches using normalizing flows can accurately evaluate sample distributions, mapping normal features to the normal distribution and anomalous features outside it. Consequently, this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network (NF-BMR). It utilizes pre-trained Convolutional Neural Networks (CNN) and normalizing flows to construct discriminative source and target domain feature spaces. Additionally, to better learn feature information in both domain spaces, we propose the Bidirectional Mapping Residual Network (BMR), which maps sample features to these two spaces… More > Graphic Abstract

    A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

  • Open Access

    ARTICLE

    Defect Detection Model Using Time Series Data Augmentation and Transformation

    Gyu-Il Kim1, Hyun Yoo2, Han-Jin Cho3, Kyungyong Chung4,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1713-1730, 2024, DOI:10.32604/cmc.2023.046324

    Abstract Time-series data provide important information in many fields, and their processing and analysis have been the focus of much research. However, detecting anomalies is very difficult due to data imbalance, temporal dependence, and noise. Therefore, methodologies for data augmentation and conversion of time series data into images for analysis have been studied. This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance, temporal dependence, and robustness to noise. The method of data augmentation is set as the addition of noise. It involves adding Gaussian noise, with the noise… More >

  • Open Access

    ARTICLE

    Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

    Xin Fan1,2, Shuqing Zhang1,2,*, Kaisheng Wu1,2, Wei Zheng1,2, Yu Ge1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187

    Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique (SMOTE)… More >

  • Open Access

    ARTICLE

    Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNN Model

    Qi Zhuang1,*, Dong Liu2, Zhuo Chen3

    Energy Engineering, Vol.121, No.3, pp. 821-834, 2024, DOI:10.32604/ee.2023.044054

    Abstract Oil and gas pipelines are affected by many factors, such as pipe wall thinning and pipeline rupture. Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management. Aiming at the shortcomings of the BP Neural Network (BPNN) model, such as low learning efficiency, sensitivity to initial weights, and easy falling into a local optimal state, an Improved Sparrow Search Algorithm (ISSA) is adopted to optimize the initial weights and thresholds of BPNN, and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established. Taking 61 sets of pipelines blasting test data… More >

  • Open Access

    ARTICLE

    A Composite Transformer-Based Multi-Stage Defect Detection Architecture for Sewer Pipes

    Zifeng Yu1, Xianfeng Li1,*, Lianpeng Sun2, Jinjun Zhu2, Jianxin Lin3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 435-451, 2024, DOI:10.32604/cmc.2023.046685

    Abstract Urban sewer pipes are a vital infrastructure in modern cities, and their defects must be detected in time to prevent potential malfunctioning. In recent years, to relieve the manual efforts by human experts, models based on deep learning have been introduced to automatically identify potential defects. However, these models are insufficient in terms of dataset complexity, model versatility and performance. Our work addresses these issues with a multi-stage defect detection architecture using a composite backbone Swin Transformer. The model based on this architecture is trained using a more comprehensive dataset containing more classes of defects. By ablation studies on the… More >

  • Open Access

    ARTICLE

    SDH-FCOS: An Efficient Neural Network for Defect Detection in Urban Underground Pipelines

    Bin Zhou, Bo Li*, Wenfei Lan, Congwen Tian, Wei Yao

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 633-652, 2024, DOI:10.32604/cmc.2023.046667

    Abstract Urban underground pipelines are an important infrastructure in cities, and timely investigation of problems in underground pipelines can help ensure the normal operation of cities. Owing to the growing demand for defect detection in urban underground pipelines, this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector (FCOS), called spatial pyramid pooling-fast (SPPF) feature fusion and dual detection heads based on FCOS (SDH-FCOS) model. This study improved the feature fusion component of the model network based on FCOS, introduced an SPPF network structure behind the last output feature layer of the… More >

  • Open Access

    ARTICLE

    Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

    Xinyu Hu, Defeng Kong*, Xiyang Liu, Junwei Zhang, Daode Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376

    Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention network; then the improved K-means-SENet… More >

  • Open Access

    ARTICLE

    Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

    Bowen Yu, Chunli Xie*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1329-1343, 2024, DOI:10.32604/cmc.2023.046248

    Abstract With the advent of Industry 4.0, marked by a surge in intelligent manufacturing, advanced sensors embedded in smart factories now enable extensive data collection on equipment operation. The analysis of such data is pivotal for ensuring production safety, a critical factor in monitoring the health status of manufacturing apparatus. Conventional defect detection techniques, typically limited to specific scenarios, often require manual feature extraction, leading to inefficiencies and limited versatility in the overall process. Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes. Our proposed approach encompasses a suite… More >

  • Open Access

    ARTICLE

    Software Defect Prediction Method Based on Stable Learning

    Xin Fan1,2,3, Jingen Mao2,3,*, Liangjue Lian2,3, Li Yu1, Wei Zheng2,3, Yun Ge2,3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 65-84, 2024, DOI:10.32604/cmc.2023.045522

    Abstract The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor. In previous software defect prediction studies, transfer learning was effective in solving the problem of inconsistent project data distribution. However, target projects often lack sufficient data, which affects the performance of the transfer learning model. In addition, the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model. To address these problems, this article propose a software defect prediction method based on stable learning (SDP-SL) that combines code… More >

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