# Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention
## 5 Proposed MADDPG Framework
The proposed architecture as a novel framework that seeks to alleviate malicious traffic from an SD-network while optimizing

Figure 15: Intrusion Detection Rates vs. Packet Transmission Rate for SA-DDPG and MA-DDPG. flow paths and transitions by updating the flow table of forwarding devices. Extensive simulation using network metrics demonstrates the significant intervention of the framework for traffic engineering and intrusion detection in SDN. A possible future research is to extend the threat determination to cover other types of network intrusions. Deploying MADDPG algorithm in distributed controllers is another interesting research that will positively affect the efficient handling of network metrics in an SDN-IoT heterogenous environment. Combining MADDPG with other meta-heuristics algorithms in SDN-IoT is another promising research area. Conceptualization, D.K.D. ; formal Analysis, D.K.D., H.N.-M. and J.D.G. ; writing--review and editing, D.K.D. and G.S.K. ; resources, D.K.D. and G.S.K. ; concept design, D.K.D. and J.D.G. ; original draft preparation, D.K.D. and J.D.G. ; supervision, J.D.G., G.S.K. and H.N.-M.; project administration, J.D.G., H.N.-M. and G.S.K. ; software, D.K.D. and H.N.-M.; validation, J.D.G., G.S.K. and H.N.-M. All authors have read and agreed to the published version of the manuscript. This research did not receive any external funding. Not applicable. The authors declare no conflict of interest. ## References

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