Mitigating DDOS Attacks in the IoT Environment

Le résumé fourni par la source

Internet of Things (IoT) networks experience reduced availability stability due to increasing vulnerability of its devices to Distributed Denial-of-Service (DDoS) attacks. The network connection of IoT devices exposes many possible attacks that make traditional defense methods ineffective during network dynamic changes. The research presents a complete DDoS protection system that employs machine learning innovations with SDN functionality in IoT networks. SDN's centralized control enables real-time anomaly detection since dynamic traffic monitoring through its system allows for swift identification and isolation of malicious traffic. Random Forests together with XGBoost and AdaBoost and Naive Bayes were chosen for their precise identification abilities and adaptive attack pattern recognition. The framework achieves minimal disruption during DDoS attack mitigation processes while maintaining low false-positive rates and demonstrating high precision according to experiment results. The complete strategy enhances both IoT network security together with resilience while delivering a scalable real-time DDoS mitigation solution which is both flexible in application. Research on the enhancement of cybersecurity for the IoT aims to develop complex machine learning frameworks while improving the overall functionality.

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