Security of 5G-V2X: Technologies, Standardization and Research Directions

Cellular-Vehicle to Everything (C-V2X) aims at resolving issues pertaining to the traditional usability of Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) networking. Specifically, C-V2X lowers the number of entities involved in vehicular communications and allows the inclusion of cellular-security solutions to be applied to V2X. For this, the evolvement of LTE-V2X is revolutionary, but it fails to handle the demands of high throughput, ultra-high reliability, and ultra-low latency alongside its security mechanisms. To counter this, 5G-V2X is considered as an integral solution, which not only resolves the issues related to LTE-V2X but also provides a function-based network setup. Several reports have been given for the security of 5G, but none of them primarily focuses on the security of 5G-V2X. This article provides a detailed overview of 5G-V2X with a security-based comparison to LTE-V2X. A novel Security Reflex Function (SRF)-based architecture is proposed and several research challenges are presented related to the security of 5G-V2X. Furthermore, the article lays out requirements of Ultra-Dense and Ultra-Secure (UD-US) transmissions necessary for 5G-V2X.Comment: 9 pages, 6 figures, Preprin

Location Privacy in LTE: A Case Study on Exploiting the Cellular Signaling Plane's Timing Advance

Location privacy is an oft-overlooked, but exceedingly important niche of the overall privacy macrocosm. An ambition of this work is to raise awareness of concerns relating to location privacy in cellular networks. To this end, we will demonstrate how user location information is leaked through a vulnerability, viz. the timing advance (TA) parameter, in the Long Term Evolution (LTE) signaling plane and how the position estimate that results from that parameter can be refined through a previously introduced method called Cellular Synchronization Assisted Refinement (CeSAR) [1]. With CeSAR, positioning accuracies that meet or exceed the FCC’s E-911 mandate are possible making CeSAR simultaneously a candidate technology for meeting the FCC’s wireless localization requirements and a demonstration of the alarming level of location information sent over the air. We also introduce a geographically diverse data set of TAs collected from actual LTE network implementations utilizing different cell phone chipsets. With this data set we show the appropriateness of modeling the error associated with a TA as normally distributed.

CogCell: Cognitive Interplay between 60GHz Picocells and 2.4/5GHz Hotspots in the 5G Era

Rapid proliferation of wireless communication devices and the emergence of a variety of new applications have triggered investigations into next-generation mobile broadband systems, i.e., 5G. Legacy 2G--4G systems covering large areas were envisioned to serve both indoor and outdoor environments. However, in the 5G-era, 80\% of overall traffic is expected to be generated in indoors. Hence, the current approach of macro-cell mobile network, where there is no differentiation between indoors and outdoors, needs to be reconsidered. We envision 60\,GHz mmWave picocell architecture to support high-speed indoor and hotspot communications. We envisage the 5G indoor network as a combination of-, and interplay between, 2.4/5\,GHz having robust coverage and 60\,GHz links offering high datarate. This requires an intelligent coordination and cooperation. We propose 60\,GHz picocellular network architecture, called CogCell, leveraging the ubiquitous WiFi. We propose to use 60\,GHz for the data plane and 2.4/5GHz for the control plane. The hybrid network architecture considers an opportunistic fall-back to 2.4/5\,GHz in case of poor connectivity in the 60\,GHz domain. Further, to avoid the frequent re-beamforming in 60\,GHz directional links due to mobility, we propose a cognitive module -- a sensor-assisted intelligent beam switching procedure -- which reduces the communication overhead. We believe that the CogCell concept will help future indoor communications and possibly outdoor hotspots, where mobile stations and access points collaborate with each other to improve the user experience.Comment: 14 PAGES in IEEE Communications Magazine, Special issue on Emerging Applications, Services and Engineering for Cognitive Cellular Systems (EASE4CCS), July 201

Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1