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Energy, water, health, transportation and emergency services act as backbones for our society. Aiming at high degrees of efficiency, these systems are increasingly automated, depending on communication systems. However, this makes these Critical Infrastructures prone to cyber attacks, resulting in data leaks, reduced performance or even total system failure. Beyond a survey of existing vulnerabilities, we provide an experimental evaluation of targeted uplink jamming against Long Term Evolution (LTE)'s air interface. Primarily, our implementations of smart attacks on the LTE Physical Uplink Control Channel (PUCCH), the Physical Uplink Shared Channel (PUSCH) as well as on the radio access procedure are outlined and tested. In exploiting the unencrypted resource assignment process, these attacks are able to target and jam specific UE resources, effectively denying uplink access. Evaluation results reveal the criticality of such attacks, severely destabilizing Critical Infrastructures, while minimizing attacker exposure. Finally we derive possible mitigations and recommendations for 5G stakeholders, which serve to improve the robustness of mission critical communications and enable the design of resilient next generation mobile networks.

Abstract-Machine-Type Communication (MTC) poses an ongoing research topic in the development of cellular communication systems. In this context, the efficient collection of extended Floating Car Data (xFCD) via Long Term Evolution (LTE) is a major challenge. In this paper, we present cluster-based xFCD collection schemes in order to form clusters with a long lifetime. As a result, the proposed clustering algorithms reduce the occurring cellular communication traffic. For the performance evaluation of the presented algorithm, a novel system model is used. By means of the system model, the user mobility can be modeled realistically and a precise quantification of the utilization of the LTE network for xFCD transmission is possible. The results show that the LTE network utilization can be significantly reduced by the proposed clustering algorithms.

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