An Empirical Study of Deep Learning-Based SS7 Attack Detection

Abstract

Signalling protocols are responsible for fundamental tasks such as initiating and terminating communication and identifying the state of the communication in telecommunication core networks. Signalling System No. 7 (SS7), Diameter, and GPRS Tunneling Protocol (GTP) are the main protocols used in 2G to 4G, while 5G uses standard Internet protocols for its signalling. Despite their distinct features, and especially their security guarantees, they are most vulnerable to attacks in roaming scenarios: the attacks that target the location update function call for subscribers who are located in a visiting network. The literature tells us that rule-based detection mechanisms are ineffective against such attacks, while the hope lies in deep learning (DL)-based solutions. In this paper, we provide a large-scale empirical study of state-of-the-art DL models, including eight supervised and five semi-supervised, to detect attacks in the roaming scenario. Our experiments use a real-world dataset and a simulated dataset for SS7, and they can be straightforwardly carried out for other signalling protocols upon the availability of corresponding datasets. The results show that semi-supervised DL models generally outperform supervised ones since they leverage both labeled and unlabeled data for training. Nevertheless, the ensemble-based supervised model NODE outperforms others in its category and some in the semi-supervised category. Among all, the semi-supervised model PReNet performs the best regarding the Recall and F1 metrics when all unlabeled data are used for training, and it is also the most stable one. Our experiment also shows that the performances of different semi-supervised models could differ a lot regarding the size of used unlabeled data in training.