  Security concerns for IoT applications have been alarming because of their
widespread use in different enterprise systems. The potential threats to these
applications are constantly emerging and changing, and therefore, sophisticated
and dependable defense solutions are necessary against such threats. With the
rapid development of IoT networks and evolving threat types, the traditional
machine learning-based IDS must update to cope with the security requirements
of the current sustainable IoT environment. In recent years, deep learning, and
deep transfer learning have progressed and experienced great success in
different fields and have emerged as a potential solution for dependable
network intrusion detection. However, new and emerging challenges have arisen
related to the accuracy, efficiency, scalability, and dependability of the
traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep
transfer learning-based dependable IDS model that outperforms several existing
approaches. The unique contributions include effective attribute selection,
which is best suited to identify normal and attack scenarios for a small amount
of labeled data, designing a dependable deep transfer learning-based ResNet
model, and evaluating considering real-world data. To this end, a comprehensive
experimental performance evaluation has been conducted. Extensive analysis and
performance evaluation show that the proposed model is robust, more efficient,
and has demonstrated better performance, ensuring dependability.
