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  • Open Access

    ARTICLE

    Deep Learning and Entity Embedding-Based Intrusion Detection Model for Wireless Sensor Networks

    Bandar Almaslukh*

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1343-1360, 2021, DOI:10.32604/cmc.2021.017914

    Abstract Wireless sensor networks (WSNs) are considered promising for applications such as military surveillance and healthcare. The security of these networks must be ensured in order to have reliable applications. Securing such networks requires more attention, as they typically implement no dedicated security appliance. In addition, the sensors have limited computing resources and power and storage, which makes WSNs vulnerable to various attacks, especially denial of service (DoS). The main types of DoS attacks against WSNs are blackhole, grayhole, flooding, and scheduling. There are two primary techniques to build an intrusion detection system (IDS): signature-based and… More >

  • Open Access

    ARTICLE

    A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

    Saleem Ibraheem Saleem1,*, Adnan Mohsin Abdulazeez1, Zeynep Orman2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2727-2754, 2021, DOI:10.32604/cmc.2021.016447

    Abstract The writer identification (WI) of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations. It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies, including old national and religious archives. In this study, we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on blocks. This modified method is combined with a new effective rotation model to achieve an… More >

  • Open Access

    ARTICLE

    Network Log-Based SSH Brute-Force Attack Detection Model

    Jeonghoon Park1, Jinsu Kim1, B. B. Gupta2, Namje Park1,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 887-901, 2021, DOI:10.32604/cmc.2021.015172

    Abstract The rapid advancement of IT technology has enabled the quick discovery, sharing and collection of quality information, but has also increased cyberattacks at a fast pace at the same time. There exists no means to block these cyberattacks completely, and all security policies need to consider the possibility of external attacks. Therefore, it is crucial to reduce external attacks through preventative measures. In general, since routers located in the upper part of a firewall can hardly be protected by security systems, they are exposed to numerous unblocked cyberattacks. Routers block unnecessary services and accept necessary… More >

  • 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, More >

  • Open Access

    ARTICLE

    M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

    Jae Dong Lee1,2, Hyo Soung Cha1, Shailendra Rathore2, Jong Hyuk Park2,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1537-1553, 2021, DOI:10.32604/cmc.2021.014774

    Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed… More >

  • Open Access

    ARTICLE

    Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms

    Nancy Awadallah Awad*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 979-990, 2021, DOI:10.32604/cmc.2021.014307

    Abstract After the digital revolution, large quantities of data have been generated with time through various networks. The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques. While Intrusion Detection Systems (IDSs) secure resources against threats, they still face challenges in improving detection accuracy, reducing false alarm rates, and detecting the unknown ones. This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection. Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99… More >

  • Open Access

    ARTICLE

    Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

    Hyun Kyu Shin1, Si Woon Lee2, Goo Pyo Hong3, Lee Sael2, Sang Hyo Lee4, Ha Young Kim5,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2493-2507, 2021, DOI:10.32604/cmc.2021.014170

    Abstract The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage… More >

  • Open Access

    ARTICLE

    Canny Edge Detection Model in MRI Image Segmentation Using Optimized Parameter Tuning Method

    Meera Radhakrishnan1,*, Anandan Panneerselvam2, Nandhagopal Nachimuthu3

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1185-1199, 2020, DOI:10.32604/iasc.2020.012069

    Abstract Image segmentation is a crucial stage in the investigation of medical images and is predominantly implemented in various medical applications. In the case of investigating MRI brain images, the image segmentation is mainly employed to measure and visualize the anatomic structure of the brain that underwent modifications to delineate the regions. At present, distinct segmentation approaches with various degrees of accurateness and complexities are available. But, it needs tuning of various parameters to obtain optimal results. The tuning of parameters can be considered as an optimization issue using a similarity function in solution space. This… More >

  • Open Access

    ARTICLE

    Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

    Pengshou Xie1, Guoqiang Ma1, *, Tao Feng1, Yan Yan1, 2, Xueming Han1

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1127-1137, 2020, DOI:10.32604/cmc.2020.09695

    Abstract Undoubtedly, uncooperative or malicious nodes threaten the safety of Internet of Vehicles (IoV) by destroying routing or data. To this end, some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication, data, energy, etc., to detect and evaluate vehicle nodes. However, it is difficult to effectively assess the trust level of a vehicle node only by message forwarding, data consistency, and energy sufficiency. In order to resolve these problems, a novel mechanism and a new trust calculating model is proposed in this… More >

  • Open Access

    ARTICLE

    Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

    Duo Ma1, 2, 3, Hongyuan Fang1, 2, 3, *, Binghan Xue1, 2, 3, Fuming Wang1, 2, 3, Mohammed A. Msekh4, Chiu Ling Chan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1267-1291, 2020, DOI:10.32604/cmes.2020.09122

    Abstract The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to… More >

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