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  • Open Access


    Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

    Khaled M. Alalayah1, Fatma S. Alrayes2, Mohamed K. Nour3, Khadija M. Alaidarous1, Ibrahim M. Alwayle1, Heba Mohsen4, Ibrahim Abdulrab Ahmed5, Mesfer Al Duhayyim6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3103-3119, 2023, DOI:10.32604/csse.2023.034034

    Abstract Malware is a ‘malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack… More >

  • Open Access


    Wireless Sensor Security Issues on Data Link Layer: A Survey

    Muhammad Zulkifl Hasan*, Zurina Mohd Hanapi, Muhammad Zunnurain Hussain

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4065-4084, 2023, DOI:10.32604/cmc.2023.036444

    Abstract A computer network can be defined as many computing devices connected via a communication medium like the internet. Computer network development has proposed how humans and devices communicate today. These networks have improved, facilitated, and made conventional forms of communication easier. However, it has also led to uptick in-network threats and assaults. In 2022, the global market for information technology is expected to reach $170.4 billion. However, in contrast, 95% of cyber security threats globally are caused by human action. These networks may be utilized in several control systems, such as home-automation, chemical and physical… More >

  • Open Access


    Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

    Fatma S. Alrayes1, Najm Alotaibi2, Jaber S. Alzahrani3, Sana Alazwari4, Areej Alhogail5, Ali M. Al-Sharafi6, Mahmoud Othman7, Manar Ahmed Hamza8,*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3037-3052, 2023, DOI:10.32604/csse.2023.033970

    Abstract Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla… More >

  • Open Access


    Topological Evaluation of Certain Computer Networks by Contraharmonic-Quadratic Indices

    Ahmed M. Alghamdi1,*, Khalid Hamid2, Muhammad Waseem Iqbal3, M. Usman Ashraf4, Abdullah Alshahrani5, Adel Alshamrani6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3795-3810, 2023, DOI:10.32604/cmc.2023.033976

    Abstract In various fields, different networks are used, most of the time not of a single kind; but rather a mix of at least two networks. These kinds of networks are called bridge networks which are utilized in interconnection networks of PC, portable networks, spine of internet, networks engaged with advanced mechanics, power generation interconnection, bio-informatics and substance intensify structures. Any number that can be entirely calculated by a graph is called graph invariants. Countless mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty years. Nevertheless, no trustworthy evaluation has… More >

  • Open Access


    K-Banhatti Sombor Invariants of Certain Computer Networks

    Khalid Hamid1, Muhammad Waseem Iqbal2,*, Abaid Ur Rehman Virk3, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 15-31, 2022, DOI:10.32604/cmc.2022.028406

    Abstract Any number that can be uniquely determined by a graph is called a graph invariant. During the last twenty years’ countless mathematical graph invariants have been characterized and utilized for correlation analysis. However, no reliable examination has been embraced to decide, how much these invariants are related with a network graph or molecular graph. In this paper, it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science, mathematics, chemistry, pharmacy, informatics and biology in context with physical and chemical structures and networks, because k-banhatti sombor More >

  • Open Access


    An Optimal Framework for SDN Based on Deep Neural Network

    Abdallah Abdallah1, Mohamad Khairi Ishak2, Nor Samsiah Sani3, Imran Khan4, Fahad R. Albogamy5, Hirofumi Amano6, Samih M. Mostafa7,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1125-1140, 2022, DOI:10.32604/cmc.2022.025810

    Abstract Software-defined networking (SDN) is a new paradigm that promises to change by breaking vertical integration, decoupling network control logic from the underlying routers and switches, promoting (logical) network control centralization, and introducing network programming. However, the controller is similarly vulnerable to a “single point of failure”, an attacker can execute a distributed denial of service (DDoS) attack that invalidates the controller and compromises the network security in SDN. To address the problem of DDoS traffic detection in SDN, a novel detection approach based on information entropy and deep neural network (DNN) is proposed. This approach… More >

  • Open Access


    An Optimal Scheme for WSN Based on Compressed Sensing

    Firas Ibrahim AlZobi1, Ahmad Ali AlZubi2,*, Kulakov Yurii3, Abdullah Alharbi2, Jazem Mutared Alanazi2, Sami Smadi1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1053-1069, 2022, DOI:10.32604/cmc.2022.025555

    Abstract Wireless sensor networks (WSNs) is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks, bio-medical engineering, agriculture, industry and many more. It has been used in the internet-of-things (IoTs) applications. A method for data collecting utilizing hybrid compressive sensing (CS) is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load. Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes, and then the… More >

  • Open Access


    Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

    Muhammad Adnan Khan1,*, Abdur Rehman2, Khalid Masood Khan1, Mohammed A. Al Ghamdi3, Sultan H. Almotiri3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 467-480, 2021, DOI:10.32604/cmc.2020.013121

    Abstract Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations… More >

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