  The widespread integration of Internet of Things (IoT) devices across all
facets of life has ushered in an era of interconnectedness, creating new
avenues for cybersecurity challenges and underscoring the need for robust
intrusion detection systems. However, traditional security systems are designed
with a closed-world perspective and often face challenges in dealing with the
ever-evolving threat landscape, where new and unfamiliar attacks are constantly
emerging. In this paper, we introduce a framework aimed at mitigating the open
set recognition (OSR) problem in the realm of Network Intrusion Detection
Systems (NIDS) tailored for IoT environments. Our framework capitalizes on
image-based representations of packet-level data, extracting spatial and
temporal patterns from network traffic. Additionally, we integrate stacking and
sub-clustering techniques, enabling the identification of unknown attacks by
effectively modeling the complex and diverse nature of benign behavior. The
empirical results prominently underscore the framework's efficacy, boasting an
impressive 88\% detection rate for previously unseen attacks when compared
against existing approaches and recent advancements. Future work will perform
extensive experimentation across various openness levels and attack scenarios,
further strengthening the adaptability and performance of our proposed solution
in safeguarding IoT environments.
