  The Internet of Things (IoT) has been introduced as a breakthrough technology
that integrates intelligence into everyday objects, enabling high levels of
connectivity between them. As the IoT networks grow and expand, they become
more susceptible to cybersecurity attacks. A significant challenge in current
intrusion detection systems for IoT includes handling imbalanced datasets where
labeled data are scarce, particularly for new and rare types of cyber attacks.
Existing literature often fails to detect such underrepresented attack classes.
This paper introduces a novel intrusion detection approach designed to address
these challenges. By integrating Self Supervised Learning (SSL), Few Shot
Learning (FSL), and Random Forest (RF), our approach excels in learning from
limited and imbalanced data and enhancing detection capabilities. The approach
starts with a Deep Infomax model trained to extract key features from the
dataset. These features are then fed into a prototypical network to generate
discriminate embedding. Subsequently, an RF classifier is employed to detect
and classify potential malware, including a range of attacks that are
frequently observed in IoT networks. The proposed approach was evaluated
through two different datasets, MaleVis and WSN-DS, which demonstrate its
superior performance with accuracies of 98.60% and 99.56%, precisions of 98.79%
and 99.56%, recalls of 98.60% and 99.56%, and F1-scores of 98.63% and 99.56%,
respectively.
