  The number of Internet of Things (IoT) devices being deployed into networks
is growing at a phenomenal level, which makes IoT networks more vulnerable in
the wireless medium. Advanced Persistent Threat (APT) is malicious to most of
the network facilities and the available attack data for training the machine
learning-based Intrusion Detection System (IDS) is limited when compared to the
normal traffic. Therefore, it is quite challenging to enhance the detection
performance in order to mitigate the influence of APT. Therefore, Prior
Knowledge Input (PKI) models are proposed and tested using the SCVIC-APT- 2021
dataset. To obtain prior knowledge, the proposed PKI model pre-classifies the
original dataset with unsupervised clustering method. Then, the obtained prior
knowledge is incorporated into the supervised model to decrease training
complexity and assist the supervised model in determining the optimal mapping
between the raw data and true labels. The experimental findings indicate that
the PKI model outperforms the supervised baseline, with the best macro average
F1-score of 81.37%, which is 10.47% higher than the baseline.
