Discovery of DoS attacks by the ZED-IDS anomaly detector
What is it about?
Intrusion detection is one of the major challenges in today’s security industry. In this context, deep learning networks may play an important role, by analyzing network flows and classifying them as "normal" or "intrusion". This work presents a deep learning architecture for network attacks detection. The problem is tackled as a semi-supervised task, and the anomaly detector is based on a deep Autoencoder. The results obtained show the potential of the proposal for 0-day attack detection.
Why is it important?
In this work, we have presented the ZED-IDS AE, an anomaly detector of DoS attacks based on a deep Autoencoder and semi-supervised learning. All our tests and experimentations were based on the use of the CICIDS2017 dataset, which was preferred to other datasets, widely used but obsolete. The proposed solution achieved a detection accuracy of 95.73%, showing its ability to recognize "never-seen-before" attacks. This makes it potentially useful for the recognition of 0-day attacks.