Chi-Yu Li

Sze-Ann Chen, Zhi-Yi Chin, Kui-Yuan Chen et al.

Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world systems. Attackers can implant backdoors into these models, causing them to behave normally under typical conditions, but execute malicious behaviors when specific triggers are activated. In this work, we propose Plan2Cleanse, a test-time detection and mitigation framework that adapts Monte Carlo Tree Search to efficiently identify and neutralize RL backdoor attacks without requiring model retraining. Our approach recasts backdoor detection as a planning problem, enabling systematic exploration of temporally extended trigger sequences while maintaining black-box access to the target policy. By leveraging the detection results, Plan2Cleanse can further achieve efficient mitigation through tree-search preventive replanning. We evaluated our method in competitive MuJoCo environments, simulated O-RAN wireless networks, and Atari games. Plan2Cleanse achieves substantial improvements, increasing trigger detection success rates by more than 61.4 percentage points in stealthy O-RAN scenarios and improving win rates from 35\% to 53\% in competitive Humanoid environments. These results demonstrate the effectiveness of our test-time defense approach and highlight the importance of proactive defenses against backdoor threats in RL deployments. Our implementation is publicly available at https://github.com/rl-bandits-lab/RL-Backdoor.

Li-Chun Wang, Chuan-Chi Lai, Hong-Han Shuai et al.

Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd. The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations can be accurately deployed to meet large and dynamic traffic demands. Artificial intelligence can solve these problems by analyzing the collected data, and then adjust the system parameters in the framework of Self-Organizing Network (SON) to achieve the goals of self-configuration, self-optimization, and self-healing. With the help of AI technologies, 5G networks can become more intelligent. This paper aims to provide a new type of service, On-Demand Aerial Base Station as a Service. This work needs to overcome the following five technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning and modeling, 3D placement of aerial base stations and the integration of WiFi front-haul and millimeter wave/WiGig back-haul networks.

Tian Xie, Guan-Hua Tu, Bangjie Yin et al.

Since 2016, 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. We conduct the first study of exploring 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 and uncover four vulnerabilities which stem from improper standard designs, device implementation issues and network operation slips. By exploiting the vulnerabilities, together with several state-of-the-art computer visual recognition technologies, we devise two proof-of-concept attacks: user privacy leakage and telephony harassment or denial of voice service (THDoS); both of them can bypass the security defenses deployed on mobile devices and the network infrastructure. We have confirmed their feasibility and simplicity using real-world experiments, as well as assessed their potential damages and proposed recommended solutions.

Xinyu Lei, Guan-Hua Tu, Alex X. Liu et al.

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 service by using Amazon Alexa as a case study. We disclose three security vulnerabilities which root in the insecure access control of Alexa services. 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 Alexa device to open a door or place an order from Amazon.com. The insecure access control is that the Alexa device not only relies on a single-factor authentication but also takes voice commands even if no people are around. 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 Alexa 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 the laboratory and real world settings.

Guan-Hua Tu, Yuanjie Li, Chunyi Peng et al.

Mobile Internet is becoming the norm. With more personalized mobile devices in hand, many services choose to offer alternative, usually more convenient, approaches to authenticating and delivering the content between mobile users and service providers. One main option is to use SMS (i.e., short messaging service). Such carrier-grade text service has been widely used to assist versatile mobile services, including social networking, banking, to name a few. Though the text service can be spoofed via certain Internet text service providers which cooperated with carriers, such attacks haven well studied and defended by industry due to the efforts of research community. However, as cellular network technology advances to the latest IP-based 4G LTE, we find that these mobile services are somehow exposed to new threats raised by this change, particularly on 4G LTE Text service (via brand-new distributed Mobile-Initiated Spoofed SMS attack which is not available in legacy 2G/3G systems). The reason is that messaging service over LTE shifts from the circuit-switched (CS) design to the packet-switched (PS) paradigm as 4G LTE supports PS only. Due to this change, 4G LTE Text Service becomes open to access. However, its shields to messaging integrity and user authentication are not in place. As a consequence, such weaknesses can be exploited to launch attacks (e.g., hijack Facebook accounts) against a targeted individual, a large scale of mobile users and even service providers, from mobile devices. Current defenses for Internet-Initiated Spoofed SMS attacks cannot defend the unprecedented attack. Our study shows that 53 of 64 mobile services over 27 industries are vulnerable to at least one threat. We validate these proof-of-concept attacks in one major US carrier which supports more than 100 million users. We finally propose quick fixes and discuss security insights and lessons we have learnt.