Overall, the results show that KNIMEs no-code workflow framework can offer production-level DDoS detection comparable to conventional code-based techniques, providing a workable and extremely accurate way to safeguard programmable networks.
Distributed Denial-of-Service (DDoS) attacks remain one of the most significant cyber threats faced by Software-Defined Networking (SDN) architectures, essentially because of the salient decoupling of the control and data planes. This study examines the implications of DDoS attacks on the SDN data plane and evaluates t...
Kamal Singh, Brijesh Kumar· international journal of eng...· 0 citations
An explainable machine learning-based framework for accurate, transparent, and reliable DDoS attack detection in an SDN environment that combines reliable DDoS detection with transparent, analyst-oriented decision support for SDN security monitoring is developed.
J. Malik, N. Naz, Muhammad Saleem et al.· Italian National Conference...· 0 citations
Software-Defined Networking (SDN) centralizes network control in a software controller, making it a high-value target for Distributed Denial-of-Service (DDoS) attacks. Existing machine learning defences are predominantly trained offline and require costly retraining to remain effective under evolving traffic patterns a...
Sodadasu Dharma Raj, N. Goud, K. Shailaja et al.· International Conference Com...· 0 citations
Software Defined Networking (SDN) has developed as a potential networking paradigm that improves network programmability, flexibility, and centralized management. The centralized architecture of SDN presents considerable security challenges, rendering Intrusion Detection Systems (IDSs) crucial for detecting and address...
Batool Khairallah, Saif Ali· Alkadhim Journal for Compute...· 0 citations
Distributed Denial-of-Service (DDoS) attacks are a serious problem in today's networked world, especially with the rise of Internet of Things (IoT), Software-Defined Networking (SDN), cloud computing and 5G deployment that are driving the proliferation of network traffic in scale and complexity. This study suggests an...
Mohammed Wael Rasheed Allqasam· مجلة الشرق الأوسط للعلوم الإ...· 0 citations
An intelligent DDoS detection and mitigation framework that combines classical Machine Learning (ML) classifiers with Deep Learning (DL) architectures to achieve high-fidelity, low-latency attack identification across heterogeneous network topologies is presented.
S. Singh, Alok Kumar· International Journal of Com...· 0 citations
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