In cellular networks, authorities may need to physically locate user devices to track criminals or illegal equipment. This process involves authorized agents tracing devices by monitoring uplink signals with cellular operator assistance. However, tracking uncooperative uplink signal sources remains challenging, even for operators and authorities. Three key challenges persist for fine-grained localization: i) devices must generate sufficient, consistent uplink traffic over time, ii) target devices may transmit uplink signals at very low power, and iii) signals from cellular repeaters may hinder localization of the target device. While these challenges pose significant practical obstacles to localization, they have been largely overlooked in existing research.

LTE sniffers are important for security and performance analysis because they can passively capture the wireless traffic of users in LTE network. However, existing open-source LTE sniffers have only limited functionality and cannot decode data traffic. This paper introduces LTESNIFFER, the first open-source LTE sniffer that can passively decode both uplink and downlink data traffic. Implementing a sniffer is not trivial because one needs to understand detailed configurations and parameters to successfully decode each user's traffic. Using multiple techniques, we found mechanisms to understand these, which improves our decoding performance. We evaluated the performance of LTESNIFFER on both testbed and commercial network environments. We also compare the performance of LTESNIFFER with AirScope, a popular commercial LTE sniffer. Additionally, LTESNIFFER provides a proof-of-concept API with three functions that can be used for security applications, including identity mapping, identity collecting, and device capability profiling. We release LTESNIFFER as open-source for future research.

A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) provides the first line of defense to protect websites against bots and automatic crawling. Recently, audio-based CAPTCHA systems are started to use for visually impaired people in many internet services. However, with the recent improvement of speech recognition and machine learning system, audio CAPTCHAs have come to struggle to distinguish machines from users, and this situation will likely continue to worsen. Unlike conventional CAPTCHA systems, we propose a new conceptual audio CAPTCHA system, combining certain sound, which is only understandable by a machine. Our experiment results demonstrate that the tested speech recognition systems always provide correct responses for our CAPTCHA samples while humans cannot possibly understand them. Based on this computational gap between the human and machine, we can detect bots with their correct responses, rather than their incorrect ones.