P2ADF: a privacy-preserving attack detection framework in fog-IoT environment

In recent years, the Internet of Things (IoT) has gained much popularity, increasing the flow of sensitive user data across the web. In addition, the adoption of fog and edge technologies for latency-sensitive applications aggravates the privacy issues in the scenario as the sensitive data are processed in the user vicinity. Furthermore, the presence of the processing layer near the user end increases the attack surface and thus attracts malicious or curious intruders. In this light, the authors present a stacked-ensemble privacy-preserving attack detection framework, P2ADF. The framework detects the popular man-in-the-middle (MiTM) and denial-of-service (DoS)/distributed DoS (DDoS) attacks in the fog-IoT setup with a maximum accuracy of about 99.98 percent. The proposed model is trained over benchmark datasets, say, IoTID20, TON_IoT, N-BaIoT, UNSW-NB15, and CICDDoS19. The performance of the proposed model is also compared to existing state-of-the-art approaches, and P2ADF outperforms them all.