A Network-Based Positioning Method to Locate False Base Stations

Karaçay, Leyli; Bilgin, Zeki; Gunduz, Ayse Bilge; Çomak, Pınar; Tomur, Emrah; Soykan, Elif Üstündağ; Gulen, Utku; Karakoç, Ferhat

doi:10.1109/access.2021.3103673

Cited by 19 publications

(3 citation statements)

References 26 publications

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“…A false base station attack takes advantage on the common behavior of User Equipment (UE), which tends to connect to the strongest signal available. The exploit involves broadcasting a stronger signal to lure the UE into connecting, after which various active and passive attacks can be initiated [3]. These attacks include, but are not limited to, Authentication Relay attack, International Mobile Subscriber Identifier (IMSI) Catching, and Denial of Service (DoS) attacks.…”

Section: Introductionmentioning

confidence: 99%

“…These attacks include, but are not limited to, Authentication Relay attack, International Mobile Subscriber Identifier (IMSI) Catching, and Denial of Service (DoS) attacks. As these tactics pose ongoing security challenges, sustained research efforts are imperative to develop effective countermeasures and safeguard against these threats [3].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the specified behavior rule-based false base station attack detection system has the capability to detect ongoing attacks in real-time by executing the finite state machine. This presents a distinct advantage, as it enables both users and mobile operator respond promptly and effectively previously unknown attacks [3].…”

Section: Introductionmentioning

confidence: 99%

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SMDFbs: Specification-Based Misbehavior Detection for False Base Stations

Park,

Astillo,

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%