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Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks., , , and . NetSys, page 1-6. IEEE, (2019)Securing Heterogeneous IoT With Intelligent DDoS Attack Behavior Learning., , , , , , and . IEEE Syst. J., 16 (2): 1974-1983 (2022)Federated Deep Reinforcement Learning for Traffic Monitoring in SDN-Based IoT Networks., , , , and . IEEE Trans. Cogn. Commun. Netw., 7 (4): 1048-1065 (2021)Efficient SDN-Based Traffic Monitoring in IoT Networks with Double Deep Q-Network., , , , and . CSoNet, volume 12575 of Lecture Notes in Computer Science, page 26-38. Springer, (2020)A Novel Impact Analysis Approach for SDN-based Networks., , , and . NetSoft, page 10-18. IEEE, (2019)Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework., , , , , and . (2019)Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-Learning Based Defense Framework., , , , , and . GLOBECOM, page 1-6. IEEE, (2019)A Novel Hybrid Flow-Based Handler with DDoS Attacks in Software-Defined Networking., , and . UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld, page 350-357. IEEE Computer Society, (2016)Optimizing resource allocation for elastic security VNFs in the SDNFV-enabled cloud computing., , , , and . ICOIN, page 163-166. IEEE, (2017)Probability Analysis of Successful Cyber Attacks in SDN-based Networks., , and . NFV-SDN, page 1-6. IEEE, (2018)