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IMS (IP Multimedia Subsystem) is vital for delivering IP-based multimedia services in mobile networks. Despite constant upgrades by 3GPP over the past two decades to support heterogeneous radio access networks (e.g., 4G LTE, 5G NR, and Wi-Fi) and enhance IMS security, the focus has primarily been on cellular infrastructure. Consequently, IMS security measures on mobile equipment (ME), such as smartphones, lags behind rapid technological advancements. Our study reveals that mandated IMS security measures on ME fail to keep pace, resulting in new security issues and attack vectors, including denial of service (DoS) across all networks, named SMS source spoofing, IMS user privacy spying, and covert communications over Video-over-IMS attacks. All security issues and proof-of-concept attacks have been experimentally validated in operational 5G/4G networks across various phone models and network operators. We also evaluate top-tier mobile antivirus software and find that they are unable to detect the proposed attacks, highlighting the stealth and effectiveness of these exploits. Finally, we propose and prototype standard-compliant remedies for these security issues. 

In this work, we have conducted a measurement study with three US operators to reveal three types of problematic failure handling on secondary radio access which have not been reported before. Compared to primary radio access failures, secondary radio access failures do not hurt radio access availability but significantly impact data performance, particularly when 5G is used as secondary radio access to boost throughput. Improper failure handling results in significant throughput loss, which is unnecessary in most instances. Datasets are available at https:// github.com/mssn/ scgfailure. 

In this work, we present our attempt to tackle the last-hundred-feet problem for autonomous drone delivery.We take a computer-vision based approach to progressively landing towards a convenient and safe drop-off point at all times (here, at the front/garage door). Specifically, we develop structural semantic segmentation (SSS), a new technique that leverages a single-family house structure to streamline and enhance semantic segmentation in the drop-to-door problem context.We implement SSS into an Android app; Our preliminary evaluation in a residential zone shows SSS is promising to make autonomous drop-to-door in real-time, with no need to wait for slow visual processing. Video demo is available at Youtube [5]. App is released at Github [6]. 

Carrier aggregation (CA) is an important component technology in 5G and beyond. It aggregates multiple spectrum fragments to serve a mobile device. However, the current CA suffers under both high mobility and increased spectrum space. The limitations are rooted in its sequential, cell-by-cell operations. In this work, we propose CA++, which departs from the current paradigm and explores a group-based design scheme. We thus propose new algorithms that enable concurrent channel inference by measuring one or few cells but inferring all, while minimizing measurement cost via set cover approximations. Our evaluations have confirmed the effectiveness of CA++. Our solution can also be adapted to fit in the current 5G OFDM PHY and the 3GPP framework.