IoT Ddos Attack Detection Using Machine Learning
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The distribution strategy of a botnet mainly directs its configuration, installing a support of bots for coming exploitation. In this article, we utilize the sources of pandemic modeling to IoT networks consisting of WSNs. We build a proposed framework to detect and abnormal defense activities. According to the impact of IoT-specific features like insufficient processing power, power limitations, and node density on the formation of a botnet, there are significant challenges. We use standard datasets for active two famous attacks, such as Mirai. We also used many machine learning and data mining algorithms such as LSVM, Neural Network, and Decision tree to detect abnormal activities such as DDOS features. In the experimental results, we found that the merge between random forest and decision tree achieved high accuracy to detect attacks. © 2020 IEEE.
Açıklama
4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- Istanbul -- 165025
Anahtar Kelimeler
DDoS, IOT, Machine Learning, WSNs
Kaynak
4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
