SMDFbs: Specification-Based Misbehavior Detection for False Base Stations

Abstract

The advancement of cellular communication technology has profoundly transformed human life. People can now watch high-definition videos anytime, anywhere, and aim for the implementation of advanced autonomous driving capabilities. However, the sustainability of such an environment is threatened by false base stations. False base stations execute attacks in the Radio Access Network (RAN) of cellular systems, adversely affecting the network or its users. To address this challenge, we propose a behavior rule specification-based false base station detection system, SMDFbs. We derive behavior rules from the normal operations of base stations and convert these rules into a state machine. Based on this state machine, we detect network anomalies and mitigate threats. We conducted experiments detecting false base stations in a 5G RAN simulator, comparing our system with seven machine learning-based detection techniques. The experimental results showed that our proposed system achieved a detection accuracy of 98% and demonstrated lower overhead compared to other algorithms.

Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

“…With the cheaper deployment of mobile networks for SHSs, there has been an increase in fake or rogue base stations by cybercriminals, jeopardizing patient privacy and exposing sensitive healthcare data. Liu et al [64] and Park et al [69] defined fake or rogue base stations as the use of a software-defined radio to create fraudulent cell phone towers that impersonate the functionality of authentic base stations in a wireless network by tricking users within a certain radius to connect to them. Base stations are vital in wireless communication systems connecting mobile devices and the core network infrastructure.…”

“…Installing a fake base station near smart healthcare will help cyber criminals intercept and alter healthcare information sent between medical devices and legitimate base stations and trick patients and healthcare professionals into connections to unauthentic healthcare services to interfere with the confidentiality, integrity, and availability of smart healthcare data and services. This is achieved by trailing and gathering international mobile subscriber identity data from patients' and healthcare professionals' mobile devices [59][64] [69]. Adversaries can use compromised access control and authentication mechanisms to gain unauthorized access to SHSs through fake base stations [64].…”