Members of the Department for Information and Communication Traffic have published a scientific paper entitled "Boosting-based DDoS Detection in Internet of Things Systems" in the prestigious IEEE Internet of Things journal by I. Cvitić, D. Peraković, B. B. Gupta and K. K. R. Choo.
The prestige and importance of the journal is visible through the echo factor IF (2020) = 9,936 and the affiliation of the Q1 quartile to SCImago.
The published work is the result of extensive research within the project financed by University of Zagreb "Challenges of information and communication networks and technologies, services and user equipment in establishing the Society 5.0 environment" as well as quality cooperation with colleagues from India and the USA.
Abstract— 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.