RepuTE: A soft voting ensemble learning framework for reputation-based attack detection in fog-IoT milieu

The impetuous expansion of the Internet of Things (IoT) network has resulted in a noticeable increase in the production of sensitive user data. With this, to meet the demand for real-time response, a processing layer is introduced near the user end which is known as the fog computing layer. The fog layer lies in the user's vicinity and thus highly attracts malicious and/or curious intruders. As a result, the trust of the network gets negatively impacted. Motivated by the aforementioned issue, the authors consider Reputation-based trust and propose a RepuTE Framework in the Fog-IoT domain. The given framework consists of a soft voting ensemble learning model that classifies and predicts two popular reputation-based attacks namely, DoS/ DDoS and Sybil attacks. Furthermore, a novel feature selection technique is also presented that selects the most relevant features well in advance. The performance evaluation is done on NSL-KDD, CICDDOS2019, IoTID20, NBaIoT2018, TON_IoT, and UNSW_NB15 benchmarked IoT and network traffic datasets. The comprehensive performance analysis depicts that the proposed model attains 99.99% accuracy and outperforms other recent state-of-the-art works. This indicates the potential of the proposed approach for reputation-based attack filtration in the IoT domain.