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  • Open Access


    Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

    Sultan Alkhliwi*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4735-4752, 2023, DOI:10.32604/cmc.2023.037141

    Abstract Software-defined networking (SDN) algorithms are gaining increasing interest and are making networks flexible and agile. The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components, enabling flexible and dynamic network management. A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers. The deployment of the controller—that is, the controller placement problem (CPP)—becomes a vital model challenge. Through the advancements of blockchain technology, data integrity between nodes can be enhanced with no requirement for… More >

  • Open Access


    ProbD: Faulty Path Detection Based on Probability in Software-Defined Networking

    Jiangyuan Yao1, Jiawen Wang1, Shuhua Weng1, Minrui Wang1, Deshun Li1,*, Yahui Li2, Xingcan Cao3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1783-1796, 2023, DOI:10.32604/iasc.2023.034265

    Abstract With the increasing number of switches in Software-Defined Networking (SDN), there are more and more faults rising in the data plane. However, due to the existence of link redundancy and multi-path forwarding mechanisms, these problems cannot be detected in time. The current faulty path detection mechanisms have problems such as the large scale of detection and low efficiency, which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN. Concerning this issue, we propose an efficient network path fault testing model ProbD based on probability detection. This model achieves a high probability of detecting arbitrary path… More >

  • Open Access


    Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

    Prohim Tam1, Sa Math1, Seokhoon Kim1,2,*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2141-2154, 2023, DOI:10.32604/csse.2023.030984

    Abstract Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to… More >

  • Open Access


    Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

    Aqsa Aqdus1, Rashid Amin1,*, Sadia Ramzan1, Sultan S. Alshamrani2, Abdullah Alshehri3, El-Sayed M. El-kenawy4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1413-1435, 2023, DOI:10.32604/cmc.2023.031719

    Abstract The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane’s forwarding capabilities.… More >

  • Open Access


    Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

    Mohammad Adnan Aladaileh, Mohammed Anbar*, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, Shadi Al-Sarawn

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1403-1416, 2022, DOI:10.32604/cmc.2022.029369

    Abstract The emergence of a new network architecture, known as Software Defined Networking (SDN), in the last two decades has overcome some drawbacks of traditional networks in terms of performance, scalability, reliability, security, and network management. However, the SDN is vulnerable to security threats that target its controller, such as low-rate Distributed Denial of Service (DDoS) attacks, The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component. Therefore, there is an urgent need to propose a detection approach for this type of attack… More >

  • Open Access


    Comprehensive DDoS Attack Classification Using Machine Learning Algorithms

    Olga Ussatova1,2, Aidana Zhumabekova1,*, Yenlik Begimbayeva2,3, Eric T. Matson4, Nikita Ussatov5

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 577-594, 2022, DOI:10.32604/cmc.2022.026552

    Abstract The fast development of Internet technologies ignited the growth of techniques for information security that protect data, networks, systems, and applications from various threats. There are many types of threats. The dedicated denial of service attack (DDoS) is one of the most serious and widespread attacks on Internet resources. This attack is intended to paralyze the victim's system and cause the service to fail. This work is devoted to the classification of DDoS attacks in the special network environment called Software-Defined Networking (SDN) using machine learning algorithms. The analyzed dataset included instances of two classes: benign and malicious. As the… More >

  • Open Access


    An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

    Ghalib H. Alshammri1,2, Amani K. Samha3, Ezz El-Din Hemdan4, Mohammed Amoon1,4, Walid El-Shafai5,6,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3529-3548, 2022, DOI:10.32604/cmc.2022.025262

    Abstract Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process. In recent times, the most complex task in Software Defined Network (SDN) is security, which is based on a centralized, programmable controller. Therefore, monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment. Consequently, this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms: K-means, Farthest First, Canopy, Density-based algorithm, and Exception-maximization (EM), using the Waikato Environment for Knowledge Analysis (WEKA) software to compare extensively between these five algorithms.… More >

  • Open Access


    Drone-based AI/IoT Framework for Monitoring, Tracking and Fighting Pandemics

    Abdelhamied A. Ateya1,2, Abeer D. Algarni1, Andrey Koucheryavy3, Naglaa. F. Soliman1,2,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4677-4699, 2022, DOI:10.32604/cmc.2022.021850

    Abstract Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two… More >

  • Open Access


    Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications in Software-Defined IoT

    Prohim Tam1, Sa Math1, Ahyoung Lee2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3319-3335, 2022, DOI:10.32604/cmc.2022.023215

    Abstract Federated learning (FL) activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes. However, in large-scale heterogeneous Internet of Things (IoT) cellular networks, massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly. This paper introduces the system model of converging software-defined networking (SDN) and network functions virtualization (NFV) to enable device/resource abstractions and provide NFV-enabled edge FL (eFL) aggregation servers for advancing automation and controllability. Multi-agent deep Q-networks (MADQNs) target to enforce a self-learning softwarization, optimize resource allocation… More >

  • Open Access


    Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

    G. R. Sreekanth1,*, S. Ahmed Najat Ahmed2, Marko Sarac3, Ivana Strumberger3, Nebojsa Bacanin3, Miodrag Zivkovic3

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 751-765, 2022, DOI:10.32604/csse.2022.020534

    Abstract Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system… More >

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