5 research outputs found
Network-Aware AutoML Framework for Software-Defined Sensor Networks
As the current detection solutions of distributed denial of service attacks
(DDoS) need additional infrastructures to handle high aggregate data rates,
they are not suitable for sensor networks or the Internet of Things. Besides,
the security architecture of software-defined sensor networks needs to pay
attention to the vulnerabilities of both software-defined networks and sensor
networks. In this paper, we propose a network-aware automated machine learning
(AutoML) framework which detects DDoS attacks in software-defined sensor
networks. Our framework selects an ideal machine learning algorithm to detect
DDoS attacks in network-constrained environments, using metrics such as
variable traffic load, heterogeneous traffic rate, and detection time while
preventing over-fitting. Our contributions are two-fold: (i) we first
investigate the trade-off between the efficiency of ML algorithms and
network/traffic state in the scope of DDoS detection. (ii) we design and
implement a software architecture containing open-source network tools, with
the deployment of multiple ML algorithms. Lastly, we show that under the denial
of service attacks, our framework ensures the traffic packets are still
delivered within the network with additional delays
Nation-wide analysis of the impact of Covid-19 pandemic on daily urology practice in Turkey
Objective To present a nation-wide analysis of the workload of urology departments in Turkey week-by-week during Covid-19 pandemic