In-device, runtime cellular network information extraction and analysis

Li, Yuanjie; Deng, Haotian; Xiangli, Yuanbo; Yuan, Zengwen; Peng, Chunyi; Lu, Songwu

doi:10.1145/2973750.2985622

Cited by 6 publications

(9 citation statements)

References 2 publications

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“…To answer RQ1 ( §VII-A), we evaluate 5G-SPECTOR against two sets of simulated attacks, including 7 known attacks in Table II and 11 unknown attacks derived as variants from them. To answer RQ2 ( §VII-B), we evaluate 5G-SPECTOR against both benign and abnormal cellular network traffic from public datasets [31], [22]. To answer RQ3 ( §VII-C), we evaluate 5G-SPECTOR with two L3 attacks fully replicated over-the-air using COTS SDRs and smartphones.…”

Section: Discussionmentioning

confidence: 99%

“…To demonstrate 5G-SPECTOR's scalability to practical cellular networks (i.e., not producing too many false alarms), we evaluated it with datasets with traces collected from realworld UEs [31], [22]. As presented in Table IV, there are 22 benign traces (BT-1 to BT-22) collected from different COTS UEs and network operators, and 9 abnormal UE traces (AT-1 to AT-9 with network failure scenarios) created by academic researchers [22].…”

Section: B Evaluation With Real-world Datasetsmentioning

confidence: 99%

“…Run-time Cellular Network Monitoring. To detect cellular network threats in real-time, one cost-effective solution is to deploy run-time monitors [52], [31], [53]. To this end, many Android-based apps were developed for monitoring IMSI catchers [23].…”

Section: Related Workmentioning

confidence: 99%

“…We first demonstrate that 5G-SPECTOR can effectively detect the 7 attacks simulated in our testbed, and can be easily extended with 90 LoC to detect additional 11 unknown attack variants. Further, we tested 5G-SPECTOR with 31 network traces collected from COTS UEs [31], [22] and show that 5G-SPECTOR scales to real-world cellular networks without reporting any false alarms. Meanwhile, we demonstrate 5G-SPECTOR's practicality in detecting two end-to-end exploits reproduced overthe-air [9].…”

Section: Introductionmentioning

confidence: 99%

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5G-Spector: An O-RAN Compliant Layer-3 Cellular Attack Detection Service

Wen,

Porras,

Yegneswaran

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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Over the past several years, the mobile security community has discovered a wide variety of exploits against link and session-establishment protocols. These exploits can be implemented on software-defined radios (SDRs) that disrupt, spoof, or flood layer-3 (L3) messages to compromise security and privacy, which still apply to the latest 5G mobile network standard. Interestingly, unlike the prior generations of closed (proprietary) mobile network infrastructures, 5G networks are migrating toward a more intelligent and open-standards-based fully interoperable mobile architecture, called Open RAN or O-RAN. The implications of transitioning mobile infrastructures to a software-defined architectural abstraction are quite significant to the INFOSEC community, as it allows us to extend the mobile data plane and control plane with security-focused protocol auditing services and exploit detection. Based on this design, we present 5G-SPECTOR, the first comprehensive framework for detecting the wide spectrum of L3 protocol exploits on O-RAN. It features a novel security audit stream called MOBIFLOW that transfers fine-grained cellular network telemetry, and a programmable control-plane xApp called MOBIEXPERT. We present an extensible prototype of 5G-SPECTOR which can detect 7 types of cellular attacks in real-time. We also demonstrate its scalability to 11 unknown attacks as well as 31 real-world cellular traces, with effective performance (high accuracy, no false alarms) and low (<2% CPU, <100 MB memory) overhead.

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Section: Discussionmentioning

confidence: 99%

Section: B Evaluation With Real-world Datasetsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

5G-Spector: An O-RAN Compliant Layer-3 Cellular Attack Detection Service

Wen,

Porras,

Yegneswaran

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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“…On the other hand, academic researchers, especially from the developing world, are denied access to data from the latest unlicensed deployments due to the exorbitant cost of the applications. It is noteworthy that Mobile Insight [27] and SigCap [18] are very useful opensource applications. SigCap in particular is easy to install, displays real-time network information on the mobile device, and allows passive network monitoring [18].…”

Section: B Challenge: Expensive Network Monitoring Applicationsmentioning

confidence: 99%

Cellular Operator Data Meets Counterfactual Machine Learning

Kala,

Mishra,

Sathya

et al. 2024

IEEE Access

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Unlicensed cellular networks and spectrum-sharing standards assist operators in meeting the ever-increasing demand for mobile data. However, several incumbents are already operational in these frequencies, rendering the wireless environment extremely dynamic and unpredictable. The challenges associated with unlicensed Licensed Assisted Access (LAA) operations in the 5 GHz band and New Radio in Unlicensed (NR-U) in the 6 GHz band are best addressed through a data-driven approach. This requires operator data from current cellular deployments. Further, from an operator's perspective, the precision and reliability of predictive models must be analyzed before deployment. Counterfactual machine learning is ideal for quantifying causal impact in a dynamic, unlicensed cellular environment. However, the literature lacks a framework that combines data-driven solutions, counterfactual analysis, and conventional optimization. This work contributes a dataset from the LAA networks of three major cellular operators in Chicago consisting of 15 features and 9676 samples. Additionally, it proposes a framework for analyzing the performance of unlicensed networks that leverages machine learning for predictive modeling, employs counterfactual analysis for model explainability and network performance enhancement, and utilizes optimization for validation. We show that operator data is necessary to build reliable prediction models for network throughput, signal strength, etc. Further, the impact of network parameters is shown to differ in unlicensed and licensed cellular network models. Next, a counterfactual machine learning framework is proposed to explain and analyze the predictive models. The framework proposes counterfactual policies to enhance unlicensed cellular network performance. Finally, we validate the suggested counterfactual policies through joint network optimization.

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Octopus: Exploiting the Edge Intelligence for Accessible 5G Mobile Performance Enhancement

Zhou

Pei

et al. 2023

IEEE/ACM Trans. Networking

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While 5G has rolled out since 2019 and exhibited versatile advantages, its performance under high/extreme mobility scenes (e.g., driving, high-speed railway or HSR) remains mysterious. In this work, we carry out a large-scale field-trial campaign, taking >13,000 Km round-trips on HSR moving at 250-350 Km/h, with operational 5G cellular coverage along the railway. Our empirical study reveals that coupling interaction among high mobility, 5G handover characteristics, and applications' sluggish reaction to handover, results in catastrophic damage to user experience: low TCP bandwidth utilization of 26.6% and glitchy 4K VoD streaming. To solve the problem, we propose an edge-assisted mobility management framework called Octopus. Different from previous works, Octopus aims at a standard-compatible and easy-to-deploy solution, thus we take a new design paradigm of exploiting the edge intelligence on multi-access edge computing (MEC). We realize Octopus as a universal MEC service ready for benefiting any third-party mobile applications. We prototype, deploy, and evaluate Octopus in operational 5G, which demonstrates the significant performance gain across the full-range mobile scenarios, e.g., HSR, driving, and walking.

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In-device, runtime cellular network information extraction and analysis

Cited by 6 publications

References 2 publications

5G-Spector: An O-RAN Compliant Layer-3 Cellular Attack Detection Service

5G-Spector: An O-RAN Compliant Layer-3 Cellular Attack Detection Service

Cellular Operator Data Meets Counterfactual Machine Learning

Octopus: Exploiting the Edge Intelligence for Accessible 5G Mobile Performance Enhancement

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