Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

• Published in: IEEE access Vol. 9; pp. 42236 - 42264
• Main Author: Aljuhani, Ahamed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Cyberattack; Cybersecurity; DDoS attacks and detection; Denial of service attacks; Denial-of-service attack; Economic impact; Floods; Internet of Things; Internet of Things (IoT); Machine learning; machine learning (ML); Network function virtualization; network functions virtualization (NFV); Security management; software-defined network (SDN); Software-defined networking; System effectiveness; Virtual environments

A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result...