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  • Open Access

    ARTICLE

    RP-NBSR: A Novel Network Attack Detection Model Based on Machine Learning

    Zihao Shen1,2, Hui Wang1,*, Kun Liu1, Peiqian Liu1, Menglong Ba1, MengYao Zhao3

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 121-133, 2021, DOI:10.32604/csse.2021.014988

    Abstract The rapid progress of the Internet has exposed networks to an increased number of threats. Intrusion detection technology can effectively protect network security against malicious attacks. In this paper, we propose a ReliefF-P-Naive Bayes and softmax regression (RP-NBSR) model based on machine learning for network attack detection to improve the false detection rate and F1 score of unknown intrusion behavior. In the proposed model, the Pearson correlation coefficient is introduced to compensate for deficiencies in correlation analysis between features by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm is used… More >

  • Open Access

    ARTICLE

    Anomaly Detection in ICS Datasets with Machine Learning Algorithms

    Sinil Mubarak1, Mohamed Hadi Habaebi1,*, Md Rafiqul Islam1, Farah Diyana Abdul Rahman, Mohammad Tahir2

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 33-46, 2021, DOI:10.32604/csse.2021.014384

    Abstract An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as… More >

  • Open Access

    ARTICLE

    A Generative Adversarial Networks for Log Anomaly Detection

    Xiaoyu Duan1, Shi Ying1,*, Wanli Yuan1, Hailong Cheng1, Xiang Yin2

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 135-148, 2021, DOI:10.32604/csse.2021.014030

    Abstract Detecting anomaly logs is a great significance step for guarding system faults. Due to the uncertainty of abnormal log types, lack of real anomaly logs and accurately labeled log datasets. Existing technologies cannot be enough for detecting complex and various log point anomalies by using human-defined rules. We propose a log anomaly detection method based on Generative Adversarial Networks (GAN). This method uses the Encoder-Decoder framework based on Long Short-Term Memory (LSTM) network as the generator, takes the log keywords as the input of the encoder, and the decoder outputs the generated log template. The discriminator uses the Convolutional Neural… More >

  • Open Access

    ARTICLE

    Liver-Tumor Detection Using CNN ResUNet

    Muhammad Sohaib Aslam1, Muhammad Younas1, Muhammad Umar Sarwar1, Muhammad Arif Shah2,*, Atif Khan3, M. Irfan Uddin4, Shafiq Ahmad5, Muhammad Firdausi5, Mazen Zaindin6

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1899-1914, 2021, DOI:10.32604/cmc.2021.015151

    Abstract Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies… More >

  • Open Access

    ARTICLE

    Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

    Gulzar Ahmad1, Saad Alanazi2, Madallah Alruwaili2, Fahad Ahmad3,6, Muhammad Adnan Khan4,*, Sagheer Abbas1, Nadia Tabassum5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2585-2600, 2021, DOI:10.32604/cmc.2021.015080

    Abstract Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using… More >

  • Open Access

    ARTICLE

    Deep Learning Approach for COVID-19 Detection in Computed Tomography Images

    Mohamad Mahmoud Al Rahhal1, Yakoub Bazi2,*, Rami M. Jomaa3, Mansour Zuair2, Naif Al Ajlan2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2093-2110, 2021, DOI:10.32604/cmc.2021.014956

    Abstract With the rapid spread of the coronavirus disease 2019 (COVID-19) worldwide, the establishment of an accurate and fast process to diagnose the disease is important. The routine real-time reverse transcription-polymerase chain reaction (rRT-PCR) test that is currently used does not provide such high accuracy or speed in the screening process. Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques. In this study, a new convolutional neural network (CNN) framework for COVID-19 detection using computed tomography (CT) images is proposed. The EfficientNet architecture is applied as the backbone structure of the proposed network,… More >

  • Open Access

    ARTICLE

    PeachNet: Peach Diseases Detection for Automatic Harvesting

    Wael Alosaimi1,*, Hashem Alyami2, M. Irfan Uddin3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1665-1677, 2021, DOI:10.32604/cmc.2021.014950

    Abstract To meet the food requirements of the seven billion people on Earth, multiple advancements in agriculture and industry have been made. The main threat to food items is from diseases and pests which affect the quality and quantity of food. Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food. Still these mechanisms require manual efforts and human expertise to diagnose diseases. In the current decade Artificial Intelligence is used to automate different processes, including agricultural processes, such as automatic harvesting. Machine Learning techniques are becoming… More >

  • Open Access

    ARTICLE

    Role of Fuzzy Approach towards Fault Detection for Distributed Components

    Yaser Hafeez1, Sadia Ali1, Nz Jhanjhi2, Mamoona Humayun3, Anand Nayyar4,5,*, Mehedi Masud6

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1979-1996, 2021, DOI:10.32604/cmc.2021.014830

    Abstract Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment. Among other communication, teamwork, and coordination problems in global software development, the detection of faults is seen as the key challenge. Thus, there is a need to ensure the reliability of component-based applications requirements. Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations. In this study, we propose an approach for fault detection from component-based systems requirements using the fuzzy logic approach and… More >

  • Open Access

    ARTICLE

    An Efficient Fuzzy Logic Fault Detection and Identification Method of Photovoltaic Inverters

    Mokhtar Aly1,2, Hegazy Rezk3,4,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2283-2299, 2021, DOI:10.32604/cmc.2021.014786

    Abstract Fuzzy logic control (FLC) systems have found wide utilization in several industrial applications. This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic (PV) inverters. Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere. Power converters represent the main parts for the grid integration of PV systems. However, PV power converters contain several power switches that construct their circuits. The power switches in PV systems are highly subjected to high stresses due to… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

    Muhammad Kashif1, Ayyaz Hussain2, Asim Munir1, Abdul Basit Siddiqui3, Aaqif Afzaal Abbasi4, Muhammad Aakif5, Arif Jamal Malik4, Fayez Eid Alazemi6, Oh-Young Song7,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2123-2139, 2021, DOI:10.32604/cmc.2021.014782

    Abstract Expression detection plays a vital role to determine the patient’s condition in healthcare systems. It helps the monitoring teams to respond swiftly in case of emergency. Due to the lack of suitable methods, results are often compromised in an unconstrained environment because of pose, scale, occlusion and illumination variations in the image of the face of the patient. A novel patch-based multiple local binary patterns (LBP) feature extraction technique is proposed for analyzing human behavior using facial expression recognition. It consists of three-patch [TPLBP] and four-patch LBPs [FPLBP] based feature engineering respectively. Image representation is encoded from local patch statistics… More >

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