Intrusion Detection in IoT Network Using Few-Shot Class Incremental Learning

The Internet of Things (IoT) is one of the most rapidly evolving technologies, impacting various industrial sectors. With its immense potential, IoT comes with crucial security concerns, and we face high-volume and diverse attacks that must be addressed in short periods, emphasizing the importance of utilizing intrusion detection solutions in IoT networks. In the initial stage of an intrusion detection system, when sufficient samples are available from the known attack classes, classic network intrusion detection methods can deliver good performance. However, the learned knowledge is no longer suitable for new types of attacks with just a few samples. On the other hand, due to the limited computing ability of edge devices in distributed IoT, only a small scale of data can be used for model training. Therefore, designing a lightweight learning scheme targeting small-scale training data is essential to train or update the model more effectively in resource-constrained devices. We propose a novel model based on Few-Shot Class Incremental Learning (FSCIL) for network intrusion detection in IoT networks. This model has been used in incremental image classification tasks. To the best of our knowledge, this is the first time that this model has been used in network intrusion detection for multi-class classification. We compare the proposed method with some state-of-the-art methods, and experimental analysis shows that our model outperforms others.