A Data Mining Framework for Securing 3G Core Network from GTP Fuzzing Attacks

Since the emergence of 3G cellular IP networks, internet usage via 3G data services has become ubiquitous. Therefore such network is an important target for imposters who can disrupt the internet services by attacking the network core, thereby causing significant revenue losses to mobile operators. GPRS Tunneling Protocol GTP is the primary protocol used between the 3G core network nodes. In this paper, we present the design of a multi-layer framework to detect fuzzing attacks targeted to GTP control (GTP-C) packets. The framework analyzes each type of GTP-C packet separately for feature extraction, by implementing a Markov state space model at the Gn interface of the 3G core network. The Multi-layered architecture utilizes standard data mining algorithms for classification. Our analysis is based on real world network traffic collected at the Gn interface. The analysis results show that for only 5% fuzzing introduced in a packet with average size of 85 bytes, the framework detects fuzzing in GTP-C packets with 99.9% detection accuracy and 0.01% false alarm rate.