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  • Open Access

    ARTICLE

    Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

    Heba G. Mohamed1, Saud S. Alotaibi2, Majdy M. Eltahir3, Heba Mohsen4, Manar Ahmed Hamza5,*, Abu Sarwar Zamani5, Ishfaq Yaseen5, Abdelwahed Motwakel5

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5441-5458, 2022, DOI:10.32604/cmc.2022.031887 - 28 July 2022

    Abstract The Internet of Drones (IoD) offers synchronized access to organized airspace for Unmanned Aerial Vehicles (known as drones). The availability of inexpensive sensors, processors, and wireless communication makes it possible in real time applications. As several applications comprise IoD in real time environment, significant interest has been received by research communications. Since IoD operates in wireless environment, it is needed to design effective intrusion detection system (IDS) to resolve security issues in the IoD environment. This article introduces a metaheuristics feature selection with optimal stacked autoencoder based intrusion detection (MFSOSAE-ID) in the IoD environment. The… More >

  • Open Access

    ARTICLE

    Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

    Hadil Shaiba1, Jaber S. Alzahrani2, Majdy M. Eltahir3, Radwa Marzouk4, Heba Mohsen5, Manar Ahmed Hamza6,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6425-6441, 2022, DOI:10.32604/cmc.2022.031625 - 28 July 2022

    Abstract Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need… More >

  • Open Access

    ARTICLE

    Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

    Anwer Mustafa Hilal1,2,*, Aisha Hassan Abdalla Hashim1, Heba G. Mohamed3, Mohammad Alamgeer4,5, Mohamed K. Nour6, Anas Abdelrahman7, Abdelwahed Motwakel2

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6019-6035, 2022, DOI:10.32604/cmc.2022.031425 - 28 July 2022

    Abstract Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption. Since the entire procedure is valued on the basis of time, it is essential to perform adaptive estimation of the SG’s stability. Recent advancements in Machine Learning (ML) and Deep… More >

  • Open Access

    ARTICLE

    Innovative Fungal Disease Diagnosis System Using Convolutional Neural Network

    Tahir Alyas1,*, Khalid Alissa2, Abdul Salam Mohammad3, Shazia Asif4, Tauqeer Faiz5, Gulzar Ahmed6

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4869-4883, 2022, DOI:10.32604/cmc.2022.031376 - 28 July 2022

    Abstract Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections. The outer layer of your body is called the integumentary system. Your skin, hair, nails, and glands are all part of it. These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun. The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect. Heat, light, damage, and illness are all protected by it. Fungi-caused infections… More >

  • Open Access

    ARTICLE

    A Deep Learning Model for EEG-Based Lie Detection Test Using Spatial and Temporal Aspects

    Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5655-5669, 2022, DOI:10.32604/cmc.2022.031135 - 28 July 2022

    Abstract Lie detection test is highly significant task due to its impact on criminology and society. Computerized lie detection test model using electroencephalogram (EEG) signals is studied in literature. In this paper we studied deep learning framework in lie detection test paradigm. First, we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole image. Our model describes a temporal feature map of the EEG signals measured during the lie detection test. A deep learning attention model (V-TAM) extracts the temporal map vector during the learning process. This… More >

  • Open Access

    ARTICLE

    Swarm Optimization and Machine Learning for Android Malware Detection

    K. Santosh Jhansi1,2,*, P. Ravi Kiran Varma2, Sujata Chakravarty3

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6327-6345, 2022, DOI:10.32604/cmc.2022.030878 - 28 July 2022

    Abstract Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential More >

  • Open Access

    ARTICLE

    Chaotic Krill Herd with Deep Transfer Learning-Based Biometric Iris Recognition System

    Harbi Al-Mahafzah1, Tamer AbuKhalil1, Bassam A. Y. Alqaralleh2,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5703-5715, 2022, DOI:10.32604/cmc.2022.030399 - 28 July 2022

    Abstract Biometric verification has become essential to authenticate the individuals in public and private places. Among several biometrics, iris has peculiar features and its working mechanism is complex in nature. The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models. With this motivation, the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System (CKHDTL-BIRS). The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification. To achieve this, CKHDTL-BIRS model initially performs Median Filtering More >

  • Open Access

    ARTICLE

    A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

    P. Ganeshkumar*, Talal Albalawi

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5943-5959, 2022, DOI:10.32604/cmc.2022.030388 - 28 July 2022

    Abstract

    Internet of things (IoT) comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life. Generally, IoT nodes are connected in wireless media and thus they are prone to jamming attacks. In the present scenario jamming detection (JD) by using machine learning (ML) algorithms grasp the attention of the researchers due to its virtuous outcome. In this research, jamming detection is modelled as a classification problem which uses several features. Using one/two or minimum number of features produces vague results that cannot be explained. Also the

    More >

  • Open Access

    ARTICLE

    Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

    Muhammad Waqas1,3,*, Shehr Bano2, Fatima Hassan2, Shanshan Tu1, Ghulam Abbas2, Ziaul Haq Abbas4

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4489-4499, 2022, DOI:10.32604/cmc.2022.029539 - 28 July 2022

    Abstract Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the… More >

  • Open Access

    ARTICLE

    Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

    Mir Mushhood Afsar1, Shizza Saqib1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Ahmad Jalal1, Jeongmin Park4,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4763-4777, 2022, DOI:10.32604/cmc.2022.028618 - 28 July 2022

    Abstract Virtual reality is an emerging field in the whole world. The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities. Hence, the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games. The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room. To track the human movement, sensors Micro Processor Unit (MPU6050) are used… More >

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