Verification Based Scheme to Restrict IoT Attacks

In recent years, with the increased usage of the Internet of Things (IoT) devices, cyber-attacks have become a serious threat over the Internet. These devices have low memory capacity and processing power, which makes them easy targets for attackers. The research community has proposed different approaches to deal with emerging variants of attacks on IoT devices using various machine learning techniques. However, these approaches rely heavily on the classifier's categorization of a given record while ignoring its confidence. This paper proposes a verification-based scheme to reject IoT attacks by utilizing the classifier's confidence. At the same time, existing studies are evaluated using traditional cross-validation approaches (e.g., k-fold), thus, not tested against unknown attacks. We propose using the leave-one-attack-out (LOAO) cross-validation scheme to evaluate the generalizability of the application to unknown attacks. The experiments are performed on Med BIoT, a publicly available dataset consisting of three IoT attacks. The system's robustness is evaluated in terms of Receiver Operating Curves (ROC) and Equal Error rates (EERs). The results indicate a lower false-positive rate of 12.6% using the proposed verification-based approach in comparison to k-fold cross-validation.