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  • Open Access

    ARTICLE

    An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

    Younghoon Ban, Myeonghyun Kim, Haehyun Cho*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3535-3563, 2024, DOI:10.32604/cmes.2023.046658

    Abstract Antivirus vendors and the research community employ Machine Learning (ML) or Deep Learning (DL)-based static analysis techniques for efficient identification of new threats, given the continual emergence of novel malware variants. On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus systems, in particular, have been identified as vulnerable to such AEs. This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers. Our attack method utilizes seven different perturbations, including Overlay Append, Section Append, and Break Checksum,… More >

  • Open Access

    ARTICLE

    Parental Educational Expectations, Academic Pressure, and Adolescent Mental Health: An Empirical Study Based on CEPS Survey Data

    Tao Xu1,*, Fangqiang Zuo1, Kai Zheng2,*

    International Journal of Mental Health Promotion, Vol.26, No.2, pp. 93-103, 2024, DOI:10.32604/ijmhp.2023.043226

    Abstract Background: This study aimed to investigate the relationship between parental educational expectations and adolescent mental health problems, with academic pressure as a moderating variable. Methods: This study was based on the baseline data of the China Education Panel Survey, which was collected within one school year during 2013–2014. It included 19,958 samples from seventh and ninth graders, who ranged from 11 to 18 years old. After removing missing values and conducting relevant data processing, the effective sample size for analysis was 16344. The OLS (Ordinary Least Squares) multiple linear regression analysis was used to examine… More >

  • Open Access

    ARTICLE

    Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study

    Shahad Alzahrani1, Hatim Alsuwat2, Emad Alsuwat3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718

    Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework… More >

  • Open Access

    ARTICLE

    C-CORE: Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

    Wei Zhou1, Xincong Jiang2,*, Chuan Qin2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2069-2093, 2024, DOI:10.32604/cmes.2023.043248

    Abstract Edge devices, due to their limited computational and storage resources, often require the use of compilers for program optimization. Therefore, ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI. One widely used testing method for this purpose is fuzz testing, which detects bugs by inputting random test cases into the target program. However, this process consumes significant time and resources. To improve the efficiency of compiler fuzz testing, it is common practice to utilize test case prioritization techniques. Some researchers use machine learning to predict… More >

  • Open Access

    PROCEEDINGS

    Damping Properties in Gradient Nano-Grained Metals

    Sheng Qian1, Qi Tong1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.010116

    Abstract Applications such as aircrafts and electronic devices require the noise and vibration reduction without much extra burden, such as extra damping systems. High damping metallic materials that exhibit the ability to dissipate mechanical energy are potential candidates in these application via directly being part of the functional components, such as the frame materials. The energy damping in polycrystalline metals depends on the activities of defects such as dislocation and grain boundary. However, operating defects has the opposite effect on strength and damping capacity. In the quest for high damping metals, maintaining the level of strength More >

  • Open Access

    ARTICLE

    Developing Transparent IDS for VANETs Using LIME and SHAP: An Empirical Study

    Fayaz Hassan1,*, Jianguo Yu1, Zafi Sherhan Syed2, Arif Hussain Magsi3, Nadeem Ahmed4

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3185-3208, 2023, DOI:10.32604/cmc.2023.044650

    Abstract Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100%… More >

  • Open Access

    ARTICLE

    Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

    Shoaib Khan, Bilal Khan, Saifullah Jan*, Subhan Ullah, Aiman

    Journal of Cyber Security, Vol.5, pp. 47-66, 2023, DOI:10.32604/jcs.2023.045579

    Abstract Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the More >

  • Open Access

    ARTICLE

    Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

    Ayla Ocak1, Ümit Işıkdağ2, Gebrail Bekdaş1,*, Sinan Melih Nigdeli1, Sanghun Kim3, Zong Woo Geem4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2899-2924, 2024, DOI:10.32604/cmes.2023.030418

    Abstract Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures. The base isolators may lose their damping capacity over time due to environmental or dynamic effects. This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-term isolator life. In this study, an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time. With the developed model, the required damping capacity of the isolator… More >

  • Open Access

    ARTICLE

    PERFORMANCE ASSESSMENT AND EMPIRICAL CORRELATION IN A HEAT EXCHANGER SQUARE DUCT WITH DIAGONAL INSERTED GENERATORS

    Amnart Boonloi*

    Frontiers in Heat and Mass Transfer, Vol.5, pp. 1-12, 2014, DOI:10.5098/hmt.5.8

    Abstract A mathematical analysis of the heat transfer enhancement, thermal performance and flow configurations in a heat exchanger square duct with diagonal inserted plate vortex generators is presented. The 30o V–shaped baffles are modified and placed on the double sides of the thin plate or frame (with no plate) which inserted diagonally in the square duct. The effects of blockage ratio (b/H, BR), the pitch ratio (p/H, PR), flow direction (V–Downstream and V–Upstream) and configuration of inserting plate are investigated for Reynolds number based on the hydraulic diameter of the square duct, Dh, Re = 100 –… More >

  • Open Access

    ARTICLE

    Influence of Trailing-Edge Wear on the Vibrational Behavior of Wind Turbine Blades

    Yuanjun Dai1,2,*, Xin Wei1, Baohua Li1, Cong Wang1, Kunju Shi1

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.2, pp. 337-348, 2024, DOI:10.32604/fdmp.2023.042434

    Abstract To study the impact of the trailing-edge wear on the vibrational behavior of wind-turbine blades, unworn blades and trailing-edge worn blades have been assessed through relevant modal tests. According to these experiments, the natural frequencies of trailing-edge worn blades −1, −2, and −3 increase the most in the second to fourth order, the fifth order increases in the middle, and the first order increases the least. The damping ratio data indicate that, in general, the first five-order damping ratios of trailing-edge worn blades −1 and trailing-edge worn blades −2 are reduced, and the first five-order More >

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