  The rapid growth of connected devices has led to the proliferation of novel
cyber-security threats known as zero-day attacks. Traditional behaviour-based
IDS rely on DNN to detect these attacks. The quality of the dataset used to
train the DNN plays a critical role in the detection performance, with
underrepresented samples causing poor performances. In this paper, we develop
and evaluate the performance of DBN on detecting cyber-attacks within a network
of connected devices. The CICIDS2017 dataset was used to train and evaluate the
performance of our proposed DBN approach. Several class balancing techniques
were applied and evaluated. Lastly, we compare our approach against a
conventional MLP model and the existing state-of-the-art. Our proposed DBN
approach shows competitive and promising results, with significant performance
improvement on the detection of attacks underrepresented in the training
dataset.
