Network-Aware AutoML Framework for Software-Defined Sensor Networks

Abstract

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 networkaware 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 the 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.

Um Sistema Autônomo para a Predição de Ataques de DDoS em Redes Locais e Internet

“…A literatura mostra que o processo AutoML auxilia na detecção de ataques DDoS. Horsanali et al (2021) propuseram um framework de AutoML próprio para a detecção desses ataques. Eles usaram seis algoritmos de AM: regressão logística, k-nearest neighbors, Support Vector Machine, Naive Bayes, Decision Tree e o Random Forest.…”