## 8 Conclusions and Future Directions  
Software-defined networks are prone to DDoS attacks. A promising approach to address this issue is to employ classification algorithms that are capable of segregating genuine and bogus requests. Machine learning algorithms have been extensively utilized to study the phenomenon using different datasets. One of the well-established machine learning approaches is the decision tree, which has several strong features compared to other machine learning algorithms. This study evaluated several decision tree algorithms while using two recent SDN-specific datasets proposed by [PERSON] et al. [1] and [PERSON] et al. [2]. In comparison with the best performer of [PERSON] et al. [1] (i.e., SVC-RF classifier) and [PERSON] et al. [2] (random forest), as well as the AdaBoost, LogitBoost, GentleBoost, and RUSBoost algorithms, the GDT approach demonstrated better or comparable performance with respect to the standard performance measures. The GDT also showed low average training times and high throughput. Furthermore, it was verified that the proposed GDT approach creates decision trees that have low variance when it comes to predictability. Overall, it can be concluded that the GDT approach presented in this study gives better results with two different datasets, using a simple ML model that does not depend upon hyperparameter tuning. Although the accuracy achieved is not too significant, the impact of this slight increase in accuracy is still worth noticing because according to a recent report [91], the cost incurred due to DDoS attacks is usually GBP 325,000 per attack. Thus, in the case of [PERSON] et al. [1], the GDT results in an increase of 1.2% in accuracy, which actually translates to the detection of 1252 more attacks. In economic terms, this means speculatively saving a lot of money, but only if the simulated datasets in [PERSON] et al. [1] are representative of real-world attack scenarios. These results start to appear even better when one incorporates data shared by Cisco that predict a 1.5 million yearly increase in DDoS attacks [92]. In the case of [PERSON] et al. [2], it can be said that the computational cost of running a random forest is far more excessive than that of a simple decision tree. Thus, applying GDT on both datasets shows that the tree is somewhat generalizable in terms of its prediction power. One more point that needs to be mentioned is that GDT is capable of achieving the same or better accuracy while being a simpler and computationally cheaper technique, an idea which goes against _[PERSON]'s law of diminishing returns_. This law states that, if the amount of input of a production process continuously increases and all of the other production factors stay constant, the rate of growth of the output will eventually decrease. This means that returns are diminished at a certain level as a consequence of expanding the volume of input [93], as has been shown previously [94]. The work can be expanded in several dimensions. Detailed study can be performed in the direction of feature selection using better techniques as this can lead to more stable decision trees. Furthermore, analysis of the data should be performed so as to observe whether or not the datasets themselves have some intrinsic limitations. This may lead to the development of more robust datasets. The use of deep learning techniques can also be explored for the underlying problem. Conceptualization, A.Z. and S.A.K. ; methodology, A.Z. and S.A.K. ; validation, A.Z., S.A.K. and N.M.; formal analysis, A.Z. and S.A.K. ; investigation, A.Z., S.A.K., N.M., A.A.A. and S.A.; resources, A.Z., S.A.K. and N.M.; data curation, A.Z. ; writing--original draft preparation, A.Z., S.A.K., N.M., A.A.A. and S.A.; writing--review and editing, A.Z., S.A.K., N.M., A.A.A., S.A. and M.A.E. ; visualization, S.A.K. ; supervision, S.A.K. ; project administration, S.A.K. ; funding acquisition, S.A.K., N.M. and S.A. All authors have read and agreed to the published version of the manuscript.