Detection of Advanced Persistent Threats using Artificial Intelligence for Deep Packet Inspection

Advanced persistent threats (APT's) are stealthy threat actors with the skills to gain covert control of the computer network for an extended period of time. They are the highest cyber attack risk factor for large companies and states. A successful attack via an APT can cost millions of dollars, can disrupt civil life and has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. Attacks of APT's are executed in several stages as pointed out in the Lockheed Martin cyber kill chain (CKC). Each of these APT stages can potentially be identified as patterns in network traffic. Using the "APT-2020" dataset, that compiles the characteristics and stages of an APT, we carried out experiments on the detection of anomalous traffic for all APT stages. We compare several artificial intelligence models, like a stacked auto encoder, a recurrent neural network and a one class state vector machine and show significant improvements on detection in the data exfiltration stage. This dataset is the first to have a data exfiltration stage included to experiment on. According to APT-2020's authors current models have the biggest challenge specific to this stage. We introduce a method to successfully detect data exfiltration by analyzing the payload of the network traffic flow. This flow based deep packet inspection approach improves detection compared to other state of the art methods.