Boosting-based DDoS Detection in Internet of Things Systems (CROSBI ID 296838)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Cvitić, Ivan ; Peraković, Dragan ; Gupta, Brij B. ; Choo, Kim-Kwang Raymond
engleski
Boosting-based DDoS Detection in Internet of Things Systems
Distributed denial of service (DDoS) attacks remain challenging to mitigate in existing systems, including in-home networks that comprise different Internet of Things (IoT) devices. In this paper, we present a DDoS traffic detection model that uses a boosting method of logistic model trees for different IoT device classes. Specifically, a different version of the model will be generated and applied for each device class, since the characteristics of the network traffic from each device class may have subtle variation(s). As a case study, we explain how devices in a typical smart home environment can be categorized into four different classes (and in our context, Class 1 -very high level of traffic predictability, Class 2 -high level of traffic predictability, Class 3 -medium level of traffic predictability, and Class 4 -low level of traffic predictability). Findings from our evaluations show that the accuracy of our proposed approach is between 99.92% and 99.99% for these four device classes. In other words, we demonstrate that we can use device classes to help us more effectively detect DDoS traffic.
ensemble machine learning ; supervised learning ; IDS ; artificial intelligence ; cybersecurity ; DDoS ; IoT.
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
9 (3)
2022.
2109-2123
objavljeno
2327-4662
10.1109/JIOT.2021.3090909
Trošak objave rada u otvorenom pristupu
Povezanost rada
Tehnologija prometa i transport