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  • Open Access

    ARTICLE

    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

    ARTICLE

    Machine Learning with Dimensionality Reduction for DDoS Attack Detection

    Shaveta Gupta1, Dinesh Grover2, Ahmad Ali AlZubi3,*, Nimit Sachdeva4, Mirza Waqar Baig5, Jimmy Singla6

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2665-2682, 2022, DOI:10.32604/cmc.2022.025048

    Abstract With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However,… More >

  • Open Access

    ARTICLE

    Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

    Fatmah Alanazi*, Kamal Jambi, Fathy Eassa, Maher Khemakhem, Abdullah Basuhail, Khalid Alsubhi

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 923-938, 2022, DOI:10.32604/iasc.2022.024668

    Abstract Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and detection techniques that rely on… More >

  • Open Access

    ARTICLE

    DDoS Detection in SDN using Machine Learning Techniques

    Muhammad Waqas Nadeem, Hock Guan Goh*, Vasaki Ponnusamy, Yichiet Aun

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 771-789, 2022, DOI:10.32604/cmc.2022.021669

    Abstract Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a… More >

  • Open Access

    ARTICLE

    Entropy-Based Approach to Detect DDoS Attacks on Software Defined Networking Controller

    Mohammad Aladaileh1, Mohammed Anbar1,*, Iznan H. Hasbullah1, Yousef K. Sanjalawe1,2, Yung-Wey Chong1

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 373-391, 2021, DOI:10.32604/cmc.2021.017972

    Abstract The Software-Defined Networking (SDN) technology improves network management over existing technology via centralized network control. The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues. However, despite the advantages of centralized control, concern about its security is rising. The more traditional network switched to SDN technology, the more attractive it becomes to malicious actors, especially the controller, because it is the network’s brain. A Distributed Denial of Service (DDoS) attack on the controller could cripple the entire network. For that reason, researchers are always looking for ways to detect DDoS attacks against the controller with higher… More >

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