  In today's digital age, our dependence on IoT (Internet of Things) and IIoT
(Industrial IoT) systems has grown immensely, which facilitates sensitive
activities such as banking transactions and personal, enterprise data, and
legal document exchanges. Cyberattackers consistently exploit weak security
measures and tools. The Network Intrusion Detection System (IDS) acts as a
primary tool against such cyber threats. However, machine learning-based IDSs,
when trained on specific attack patterns, often misclassify new emerging
cyberattacks. Further, the limited availability of attack instances for
training a supervised learner and the ever-evolving nature of cyber threats
further complicate the matter. This emphasizes the need for an adaptable IDS
framework capable of recognizing and learning from unfamiliar/unseen attacks
over time. In this research, we propose a one-class classification-driven IDS
system structured on two tiers. The first tier distinguishes between normal
activities and attacks/threats, while the second tier determines if the
detected attack is known or unknown. Within this second tier, we also embed a
multi-classification mechanism coupled with a clustering algorithm. This model
not only identifies unseen attacks but also uses them for retraining them by
clustering unseen attacks. This enables our model to be future-proofed, capable
of evolving with emerging threat patterns. Leveraging one-class classifiers
(OCC) at the first level, our approach bypasses the need for attack samples,
addressing data imbalance and zero-day attack concerns and OCC at the second
level can effectively separate unknown attacks from the known attacks. Our
methodology and evaluations indicate that the presented framework exhibits
promising potential for real-world deployments.
