Rogue base station router detection with machine learning algorithms

A method for determining if a cellular attack from a rogue base station router (RBSR) on a cellular network is active, the method comprising:

• determining a set of broadcast data associated with a plurality of base station routers (BSRs) operating in the cellular network;
• determining, via inputting the set of broadcast data to a machine learning algorithm, anomalous broadcast data in the set of broadcast data;
• identifying a BSR associated with the anomalous broadcast data as the RBSR;
• correlating the identification of the RBSR with an event associated with a cellular attack in the cellular network;
• and
• determining that the cellular attack from the RBSR on the cellular network is active based on the correlating the identification of the RBSR with the event.

A method for determining if a cellular attack from a rogue base station router (RBSR) on a cellular network is active, the method comprising:

• determining a set of broadcast data associated with a plurality of base station routers (BSRs) operating in the cellular network;
• inserting broadcast data associated with a known RBSR into the set of broadcast data;
• inputting the set of broadcast data to a machine learning algorithm;
• determining that the machine learning algorithm similarly classifies the RBSR and the known RBSR;
• determining that an event associated with a cellular attack is present in the cellular network;
• and
• determining that the cellular attack from the RBSR on the cellular network is active based on the determining the event and the similar classification of the RBSR and the known RBSR by the machine learning algorithm.