Abstract
One of the major common assaults in the current Internet of things (IoT) network-based healthcare infrastructures is distributed denial of service (DDoS). The most challenging task in the current environment is to manage the creation of vast multimedia data from the IoT devices, which is difficult to be handled solely through the cloud. As the software defined networking (SDN) is still in its early stages, sampling-oriented measurement techniques used today in the IoT network produce low accuracy, increased memory usage, low attack detection, higher processing and network overheads. The aim of this research is to improve attack detection accuracy by using the DPTCM-KNN approach. The DPTCMKNN technique outperforms support vector machine (SVM), yet it still has to be improved. For healthcare systems, this work develops a unique approach for detecting DDoS assaults on SDN using DPTCM-KNN.
Keywords:
software-defined networking, k-nearest neighbors, distributed denial of service, DPTCM-KNN approach, SVMReferences
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14. J. Ye, X. Cheng, J. Zhu, L. Feng, L. Song, A DDoS attack detection method based on SVM in software defined network, Security and Communication Networks, 2018: Article ID 9804061, 8 pages, 2018, https://doi.org/10.1155/2018/9804061
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