kts Entropy 10.0.0.8 0.0719 10.0.0.7 0.0784 10.0.0.6 0.0719 10.0.0.5 0.0899 10.0.0.4 0.1250 10.0.0.3 0.1259 10.0.0.2 0.0899 10.0.0.1 0.1218 Total 0.6021 When choosing a threshold for both the traffic pattern, we come across with the results shown in Table 3. CONCLUSION During this research the threat detection to the controller is focused and based on that simulated the result. The quantification of early detection to first 250 to 500 packets as a range of traffic was considered. The proposed solution provides an efficient method of detection with minimal code of addition to the controller with no increase on CPU load in normal as well as attack condition. The proposed solution works particularly for SDN network. The solution detect any sort of threat Downloaded: 4/15/2022 9:44:49 AM Nova Southeastern University IP Address: 137.52.76.29 Analysis of Issues in SDN Security and Solutions Table 3. Threshold entropy result of normal and attacked traffic Type of Traffic Mini Entropy Max Entropy Mean Entropy Normal Traffic Attacked Traffic Figure 7. Entropy result for normal and attacked traffic Graph 0.425 0.215 0.205 Number of Packets Normal Traffic Attacked Traffic at the beginning stage. Entropy has been used as mechanism in DDoS detection in non-SDN network. Thus from the above simulated result we came across with the result that the entropy for attacked traffic is lower than the normal traffic We concluded the result with minimum entropy and maximum entropy. Based on the result obtained, we show the result in Figure 7 using graphical representation. REFERENCES Alsulaiman, M., Alyahya, A., Alkharboush, R., & Alghafis, N. (2009). Intrusion Detection System Using Self-Organizing Maps. 2009 Third International Conference on Network and System Security, 397-402. doi: 10.1109/NSS.2009.62 Downloaded: 4/15/2022 9:44:49 AM Nova Southeastern University IP Address: 137.52.76.29 Analysis of Issues in SDN Security and Solutions Mirkovic, J., & Reiher, P. (2004). A Taxonomy of DDoS Attack and DDoS defense Mechanisms. ACM SIGCOM