Browsing by Author "Xie, Tian"

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Exploring the Insecurity of Google Account Registration Protocol via Model Checking
(2019-01-01)
Xie, Tian
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Wang, Sihan
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Tu, Guan-Hua
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Li, Chi-Yu
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Lei, Xinyu
People nowadays use online service accounts (e.g., Gongle, Facebook, Twitter) to access certain services. Among them, Google accounts have become increasingly important for users. Not only do many Google services (e.g., Gmail, Google Voice, Google Play, etc.) require them, but many online services also trust and rely on them for operational needs (e.g., login based on Google accounts). This trend introduces a new type of attacks that create a large number of fake, but valid, Google accounts. The fake Google accounts allow the adversary to launch various cyber attacks towards Google account-related services. It motivates us to conduct an empirical security study on the Google account registration service. In this paper, we apply model checking techniques to systematically analyze the insecurity of Google account registration service. We develop a model-checking tool, GAcctAnalyzer, which consists of two phases: (1) service screening phase, which generates counterexamples from the violation of desired properties, and (2) experimental validation phase, which validates the counterexamples through real experiments. We use GAcctAnalyzer to discover four security vulnerabilities including design defects, operational slips. etc. Based on the discovered vulnerabilities, we devise two practical attacks against mobile users and Google: fake Google account generation and Google text/voice spamming attack. They can not only threaten the security of mobile applications and online services, but also cause the Google company to receive user complaints and lawsuits. We finally confirm the feasibility of these attacks through experiments, assess the real-world impact, and propose recommended solutions.
How Voice Service Threatens Cellular-Connected IoT Devices in the Operational 4G LTE Networks
(2018-01-01)
Xie, Tian
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Li, Chi-Yu
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Tang, Jiliang
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Tu, Guan-Hua
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資訊工程學系
LTE networks are rolling out cellular Internetof-Things (IoT) services. Cellular-connected IoT devices are becoming increasingly popular and the number is forecasted to grow almost fourfold from 2015 to 2021. Since they share the same infrastructure with non-IoT devices such as smartphones, we may expect no big differences between them in terms of voice/data service accounting/charging (e.g., paying for what you get) and security risks. However, our study shows that cellular IoT users may pay more than what they get, as well as are vulnerable to voice signaling spams and thus suffer from an overcharging attack which leads to financial loss or denial of service. We validate our proof-of-concept attack in a major U.S. cellular network operator which takes higher than 35% market share. We finally propose a solution to address the identified security vulnerabilities.
The Dark Side of Operational Wi-Fi Calling Services
(2018-01-01)
Xie, Tian
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Tu, Guan-Hua
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Li, Chi-Yu
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Peng, Chunyi
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Li, Jiawei
All of four major U.S. operators have rolled out nationwide Wi-Fi calling services. They are projected to surpass VoLTE (Voice over LTE) and other VoIP services in terms of mobile IP voice usage minutes in 2018. They enable mobile users to place cellular calls over Wi-Fi networks based on the 3GPP IMS (IP Multimedia Subsystem) technology. Compared with conventional cellular voice solutions, the major difference lies in that their traffic traverses untrustful Wi-Fi networks and the Internet. This exposure to insecure networks may cause the Wi-Fi calling users to suffer from security threats. Its security mechanisms are similar to the VoLTE, because both of them are supported by the IMS. They include SIM-based security, 3GPP AKA (Authentication and Key Agreement), IPSec (Internet Protocol Security), etc. However, are they sufficient to secure Wi-Fi calling services? Unfortunately, our study yields a negative answer. In this work, we explore security issues of the operational Wi-Fi calling services in three major U.S. operators' networks using commodity devices. We disclose that current Wi-Fi calling security is not bullet-proof. We uncover four vulnerabilities which stem from improper standard designs, device implementation issues and network operation slips. By exploiting them, we devise two proof-of-concept attacks: user privacy leakage and telephony harassment or denial of voice service (THDoS); they can bypass the security defenses deployed on both mobile devices and network infrastructure. We have confirmed their feasibility and simplicity using real-world experiments, as well as assessed their potential damages and proposed recommended solutions.
The Insecurity of Home Digital Voice Assistants - Vulnerabilities, Attacks and Countermeasures
(2018-01-01)
Lei, Xinyu
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Tu, Guan-Hua
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Liu, Alex X.
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Li, Chi-Yu
;
Xie, Tian
Home Digital Voice Assistants (HDVAs) are getting popular in recent years. Users can control smart devices and get living assistance through those HDVAs (e.g., Amazon Alexa, Google Home) using voice. In this work, we study the insecurity of HDVA services by using Amazon Alexa and Google Home as case studies. We disclose three security vulnerabilities which root in their insecure access control. We then exploit them to devise two proof-of-concept attacks, home burglary and fake order, where the adversary can remotely command the victim's HDVA device to open a door or place an order from Amazon.com or Google Express. The insecure access control is that IIDVA devices not only rely on a single-factor authentication but also take voice commands even if no people are around them. We thus argue that HDVAs should have another authentication factor, a physical presence based access control; that is, they can accept voice commands only when any person is detected nearby. To this end, we devise a Virtual Security Button (VsButton), which leverages the WiFi technology to detect indoor human motions. Once any indoor human motion is detected, the IllY111 device is enabled to accept voice commands. Our evaluation results show that it can effectively differentiate indoor motions from the cases of no motion and outdoor motions in both laboratory and real world settings.