An Intrusion Detection System based on Deep Belief Networks
This work addresses cybersecurity threats for network intrusion detection systems, but it is incremental as it applies an existing method (DBN) to a specific dataset with class balancing techniques.
The paper tackled the problem of detecting zero-day cyber-attacks in connected devices using Deep Belief Networks (DBN), achieving significant performance improvements on underrepresented attack samples in the CICIDS2017 dataset compared to conventional methods.
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.