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  • Open Access

    ARTICLE

    MBB-IoT: Construction and Evaluation of IoT DDoS Traffic Dataset from a New Perspective

    Yi Qing1, Xiangyu Liu2, Yanhui Du2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2095-2119, 2023, DOI:10.32604/cmc.2023.039980

    Abstract Distributed Denial of Service (DDoS) attacks have always been a major concern in the security field. With the release of malware source codes such as BASHLITE and Mirai, Internet of Things (IoT) devices have become the new source of DDoS attacks against many Internet applications. Although there are many datasets in the field of IoT intrusion detection, such as Bot-IoT, Constrained Application Protocol–Denial of Service (CoAP-DoS), and LATAM-DDoS-IoT (some of the names of DDoS datasets), which mainly focus on DDoS attacks, the datasets describing new IoT DDoS attack scenarios are extremely rare, and only N-BaIoT and IoT-23 datasets used IoT… More >

  • Open Access

    ARTICLE

    Evaluation of IoT Measurement Solutions from a Metrology Perspective

    Donatien Koulla Moulla1,2,*, Ernest Mnkandla1, Alain Abran3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2455-2479, 2023, DOI:10.32604/csse.2023.039736

    Abstract To professionally plan and manage the development and evolution of the Internet of Things (IoT), researchers have proposed several IoT performance measurement solutions. IoT performance measurement solutions can be very valuable for managing the development and evolution of IoT systems, as they provide insights into performance issues, resource optimization, predictive maintenance, security, reliability, and user experience. However, there are several issues that can impact the accuracy and reliability of IoT performance measurements, including lack of standardization, complexity of IoT systems, scalability, data privacy, and security. While previous studies proposed several IoT measurement solutions in the literature, they did not evaluate… More >

  • Open Access

    ARTICLE

    Adversarial Attack-Based Robustness Evaluation for Trustworthy AI

    Eungyu Lee, Yongsoo Lee, Taejin Lee*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1919-1935, 2023, DOI:10.32604/csse.2023.039599

    Abstract Artificial Intelligence (AI) technology has been extensively researched in various fields, including the field of malware detection. AI models must be trustworthy to introduce AI systems into critical decision-making and resource protection roles. The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI. Although various adversarial attack and defense methods are actively being studied, there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks. An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and… More >

  • Open Access

    ARTICLE

    Evaluation of Beta-Lactam Antibiotics on the Regeneration of Peanut Plants and Their Inhibitory Effect on Agrobacterium Growth

    Abraham Lamboro1,3,*, Songnan Yang1, Xueying Li1, Dan Yao2, Jun Zhang1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2489-2501, 2023, DOI:10.32604/phyton.2023.029492

    Abstract The effect of beta-lactam antibiotics on shoot induction and plantlet regeneration from cotyledonary nodes was tested using two peanut cultivars. The culture media contained 4 mg/L 6-benzylaminopurine (BAP) as the main growth regulator. Various concentrations (100–600 mg/L) of cefotaxime, carbenicillin, and timentin were applied in the culture media. In all the tested media, there were no significant differences in the shoot induction as compared to the control. However, little phytotoxic effect was observed at higher concentrations of these antibiotics in the shoot elongation media. Under shoot elongation medium, shoots turned brownish and partly died at higher concentrations where shooting rates… More >

  • Open Access

    ARTICLE

    A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

    Jintao Fu1, Yong Zhou1,*, Qian Qiu2, Guangwei Xu3, Neng Wan3

    Journal of Quantum Computing, Vol.4, No.3, pp. 147-163, 2022, DOI:10.32604/jqc.2022.038358

    Abstract In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression… More >

  • Open Access

    ARTICLE

    Multidimensional Quality Evaluation of Graduate Thesis: Based on the Probabilistic Linguistic MABAC Method

    Yuyan Luo1,2, Xiaoxu Zhang1,*, Tao Tong1, Yong Qin3,*, Zheng Yang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2049-2076, 2023, DOI:10.32604/cmes.2023.025413

    Abstract Graduate education is the main way to train high-level innovative talents, the basic layout to cope with the global talent competition, and the important cornerstone for implementing the innovation-driven development strategy and building an innovation-driven country. Therefore, graduate education is of great remarkably to the development of national education. As an important manifestation of graduate education, the quality of a graduate thesis should receive more attention. It is conducive to promoting the quality of graduates by supervising and examining the quality of the graduate thesis. For this purpose, this work is based on text mining, expert interviews, and questionnaire surveys… More >

  • Open Access

    ARTICLE

    Experimental Evaluation of Compressive Strength and Gas Permeability of Glass-Powder-Containing Mortar

    Yue Liang, Wenxuan Dai, Wei Chen*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2639-2659, 2023, DOI:10.32604/fdmp.2023.027622

    Abstract Glass powder of various particle sizes (2, 5, 10 and 15 μm) has been assessed as a possible cement substitute for mortars. Different replacement rates of cement (5%, 10%, 15%, and 20%) have been considered for all particle sizes. The accessible porosity, compressive strength, gas permeability and microstructure have been investigated accordingly. The results have shown that adding glass powder up to 20% has a significantly negative effect on the porosity and compressive strength of mortar. The compressive strength initially rises with a 5% replacement and then decreases. Similarly, the gas permeability of the mortar displays a non-monotonic behavior; first, it… More >

  • Open Access

    ARTICLE

    Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

    S. Priya1, R. S. Ponmagal2,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1895-1907, 2023, DOI:10.32604/iasc.2023.039814

    Abstract Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks. To address such threats towards cloud services, numerous techniques exist that mitigate the service threats according to different metrics. The rule-based approaches are unsuitable for new threats, whereas trust-based systems estimate trust value based on behavior, flow, and other features. However, the methods suffer from mitigating intrusion attacks at a higher rate. This article presents a novel Multi Fractal Trust Evaluation Model (MFTEM) to overcome these deficiencies. The method involves analyzing service growth,… More >

  • Open Access

    ARTICLE

    A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

    Narit Hnoohom1, Sakorn Mekruksavanich2, Anuchit Jitpattanakul3,4,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1275-1291, 2023, DOI:10.32604/iasc.2023.038584

    Abstract In recent years, as intelligent transportation systems (ITS) such as autonomous driving and advanced driver-assistance systems have become more popular, there has been a rise in the need for different sources of traffic situation data. The classification of the road surface type, also known as the RST, is among the most essential of these situational data and can be utilized across the entirety of the ITS domain. Recently, the benefits of deep learning (DL) approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods. The ability to extract important features is vital in making RST… More >

  • Open Access

    ARTICLE

    NUMERICAL ANALYSES ON VAPOR PRESSURE DROP IN A CENTERED-WICK ULTRA-THIN HEAT PIPE

    Yasushi Koitoa,*

    Frontiers in Heat and Mass Transfer, Vol.13, pp. 1-6, 2019, DOI:10.5098/hmt.13.26

    Abstract This paper describes extended numerical analyses on vapor pressure distribution in a centered-wick ultra-thin heat pipe. Analyses were conducted by using a three-dimensional model developed by the author. Numerical results were obtained changing design parameters and operating conditions of the heat pipe. Discussion was made on the heat transfer limit as well as the vapor pressure drop. Moreover, a simple method was also presented to evaluate the vapor pressure drop in the ultra-thin heat pipe. Calculated results with the simple method agreed in 10 % with the three-dimensional numerical results. More >

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