State of the Art Literature Review on Network Anomaly Detection with Deep Learning

Abstract

As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additionally, the expected number of connected devices as well as the fast-paced development caused by the Internet of Things, raises huge risks in cyber security that must be dealt with accordingly. When considering all above-mentioned reasons, there is no doubt that there is plenty of room for more advanced methods in network anomaly detection hence Deep Learning based techniques have been proposed recently in detecting anomalies.

The papers reviewed showed that different Deep Learning methods vary in their performance to detect anomalies, but neural networks capability to adapt to rapidly changing network environments by self learning, to handle multi-dimensional data and to detect previously unknown attacks gives a huge advantage for detecting sophisticated attacks such as APT.