Proactive Attack Detection at the Edge through an Ensemble Deep Learning Model
Panagiotis Fountas, Maria Papathanasaki, Kostas Kolomvatsos, Nikos Tziritas · 2021
The new form of the Web involves numerous devices present in two infrastructures, i.e., the Internet of Things (IoT) and the Edge Computing (EC) infrastructure. IoT devices are adopted to record ambient data and host lightweight processing to provide support for applications offered to end users. EC is placed between the IoT and Cloud and can be the host of more advanced processing activities. It has gained popularity due to the increased computational resources compared to the IoT and the decreased latency in the provision of responses compared to the Cloud. A high number of nodes may be present at the EC that should secure the Quality of Service (QoS) of the desired applications. Apparently, EC nodes become central points where the collected data are collected and processed. Data processing (especially when data are sensitive) imposes various security issues that should be mitigated in order to maintain high QoS levels and the uninterrupted functioning of EC nodes. In this paper, motivated by the need of the increased security, we propose an ensemble scheme for the detection of attacks in the EC. Our distributed scheme relies on the adoption of deep learning to proactively detect potential malfunctions. Our model is embedded in EC nodes and is continuously applied upon the streams of data transferred by IoT devices to the EC. We present the details of our approach and evaluate it through a variety of simulation scenarios. Our intention is to reveal the strengths and weaknesses of the provided model when adopted in a very dynamic environment like the EC.