Evolving Advanced Persistent Threat Detection Using Provenance Graph and Metric Learning
Gbadebo Ayoade, Khandakar A. Akbar, Pracheta Sahoo, Yang Gao, Anoop Singhal, Kangkook Jee, Latifur Khan, Anmol Agarwal
Advanced persistent threats (APT) have increased in recent times as a result of the rise in interest by nationstates and sophisticated corporations to obtain high profile information. Typically, APT attacks are more challenging to detect since they leverage zero-day attacks and commonly used benign tools. Furthermore, these attack campaigns are often prolonged to evade detection. We leverage an approach that uses a provenance graph to obtain execution traces of host nodes in order to detect anomalous behavior. By using the provenance graph, we extract features that are then used to train an online adaptive metric learning. Online metric learning is a deep learning method that learns a function to minimize the separation between similar classes and maximizes the separation between dis- similar instances. We compare our approach with baseline models and we show our method outperforms the baseline models by increasing detection accuracy on average by 11.3% and increases True positive rate(TPR) on average by 18.3%.
June 29-July 1, 2020
IEEE International Conference on Communications and Network Security (CNS 2020)
, Akbar, K.
, Sahoo, P.
, Gao, Y.
, Singhal, A.
, Jee, K.
, Khan, L.
and Agarwal, A.
Evolving Advanced Persistent Threat Detection Using Provenance Graph and Metric Learning, IEEE International Conference on Communications and Network Security (CNS 2020), Avignon, -1, [online], https://doi.org/10.1109/CNS48642.2020.9162264
(Accessed April 22, 2021)