Cleartext Data Transmissions in Consumer IoT Medical Devices

This paper introduces a method to capture network traffic from medical IoT devices and automatically detect cleartext information that may reveal sensitive medical conditions and behaviors. The research follows a three-step approach involving traffic collection, cleartext detection, and metadata analysis. We analyze four popular consumer medical IoT devices, including one smart medical device that leaks sensitive health information in cleartext. We also present a traffic capture and analysis system that seamlessly integrates with a home network and offers a user-friendly interface for consumers to monitor and visualize data transmissions of IoT devices in their homes.Comment: 6 pages, 5 figure

Knock! Knock! Who Is There? Investigating Data Leakage from a Medical Internet of Things Hijacking Attack

The amalgamation of Medical Internet of Things (MIoT) devices into everyday life is influencing the landscape of modern medicine. The implementation of these devices potentially alleviates the pressures and physical demands of healthcare systems through the remote monitoring of patients. However, there are concerns that the emergence of MIoT ecosystems is introducing an assortment of security and privacy challenges. While previous research has shown that multiple vulnerabilities exist within MIoT devices, minimal research investigates potential data leakage from MIoT devices through hijacking attacks. The research contribution of this paper is twofold. First, it provides a proof of concept that certain MIoT devices and their accompanying smartphone applications are vulnerable to hijacking attacks. Second, it highlights the effectiveness of using digital forensics tools as a lens to identify patient and medical device information on a hijacker’s smartphone

Privacy implications of switching ON a light bulb in the IoT world

The number of connected devices is increasing every day, creating smart homes and shaping the era of the Internet of Things (IoT), and most of the time, end-users are unaware of their impacts on privacy. In this work, we analyze the ecosystem around a Philips Hue smart white bulb in order to assess the privacy risks associated to the use of different devices (smart speaker or button) and smartphone applications to control it. We show that using different techniques to switch ON or OFF this bulb has significant consequences regarding the actors involved (who mechanically gather information on the user's home) and the volume of data sent to the Internet (we measured differences up to a factor 100, depending on the control technique we used). Even when the user is at home, these data flows often leave the user's country, creating a situation that is neither privacy friendly (and the user is most of the time ignorant of the situation), nor sovereign (the user depends on foreign actors), nor sustainable (the extra energetic consumption is far from negligible). We therefore advocate a complete change of approach, that favors local communications whenever sufficient

Your Privilege Gives Your Privacy Away: An Analysis of a Home Security Camera Service

Once considered a luxury, Home Security Cameras (HSCs) are now commonplace and constitute a growing part of the wider online video ecosystem. This paper argues that their expanding coverage and close integration with daily life may result in not only unique behavioral patterns, but also key privacy concerns. This motivates us to perform a detailed measurement study of a major HSC provider, covering 15.4M streams and 211K users. Our study takes two perspectives: (i) we explore the per-user behaviour, identifying core clusters of users; and (ii) we build on this analysis to extract and predict privacy-compromising insight. Key observations include a highly asymmetrical traffic distribution, distinct usage patterns, wasted resources and fixed viewing locations. Furthermore, we identify three privacy risks and explore them in detail. We find that paid users are more likely to be exposed to attacks due to their heavier usage patterns. We conclude by proposing simple mitigations that can alleviate these risk

Federated Agentless Detection of Endpoints Using Behavioral and Characteristic Modeling

During the past two decades computer networks and security have evolved that, even though we use the same TCP/IP stack, network traffic behaviors and security needs have significantly changed. To secure modern computer networks, complete and accurate data must be gathered in a structured manner pertaining to the network and endpoint behavior. Security operations teams struggle to keep up with the ever-increasing number of devices and network attacks daily. Often the security aspect of networks gets managed reactively instead of providing proactive protection. Data collected at the backbone are becoming inadequate during security incidents. Incident response teams require data that is reliably attributed to each individual endpoint over time. With the current state of dissociated data collected from networks using different tools it is challenging to correlate the necessary data to find origin and propagation of attacks within the network. Critical indicators of compromise may go undetected due to the drawbacks of current data collection systems leaving endpoints vulnerable to attacks. Proliferation of distributed organizations demand distributed federated security solutions. Without robust data collection systems that are capable of transcending architectural and computational challenges, it is becoming increasingly difficult to provide endpoint protection at scale. This research focuses on reliable agentless endpoint detection and traffic attribution in federated networks using behavioral and characteristic modeling for incident response

Information exposure from consumer IoT devices: a multidimensional, network-informed measurement approach

Internet of Things (IoT) devices are increasingly found in everyday homes, providing useful functionality for devices such as TVs, smart speakers, and video doorbells. Along with their benefits come potential privacy risks, since these devices can communicate information about their users to other parties over the Internet. However, understanding these risks in depth and at scale is difficult due to heterogeneity in devices' user interfaces, protocols, and functionality. In this work, we conduct a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK. Through a total of 34,586 rigorous automated and manual controlled experiments, we characterize information exposure in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, what are the IoT-device interactions that can be inferred from such content, and whether there are unexpected exposures of private and/or sensitive information (e.g., video surreptitiously transmitted by a recording device). We highlight regional differences between these results, potentially due to different privacy regulations in the US and UK. Last, we compare our controlled experiments with data gathered from an in situ user study comprising 36 participants

Developing a Systematic Process for Mobile Surveying and Analysis of WLAN security

Wireless Local Area Network (WLAN), familiarly known as Wi-Fi, is one of the most used wireless networking technologies. WLANs have rapidly grown in popularity since the release of the original IEEE 802.11 WLAN standard in 1997. We are using our beloved wireless internet connection for everything and are connecting more and more devices into our wireless networks in every form imaginable. As the number of wireless network devices keeps increasing, so does the importance of wireless network security. During its now over twenty-year life cycle, a multitude of various security measures and protocols have been introduced into WLAN connections to keep our wireless communication secure. The most notable security measures presented in the 802.11 standard have been the encryption protocols Wired Equivalent Privacy (WEP) and Wi-Fi Protected Access (WPA). Both encryption protocols have had their share of flaws and vulnerabilities, some of them so severe that the use of WEP and the first generation of the WPA protocol have been deemed irredeemably broken and unfit to be used for WLAN encryption. Even though the aforementioned encryption protocols have been long since deemed fatally broken and insecure, research shows that both can still be found in use today. The purpose of this Master’s Thesis is to develop a process for surveying wireless local area networks and to survey the current state of WLAN security in Finland. The goal has been to develop a WLAN surveying process that would at the same time be efficient, scalable, and easily replicable. The purpose of the survey is to determine to what extent are the deprecated encryption protocols used in Finland. Furthermore, we want to find out in what state is WLAN security currently in Finland by observing the use of other WLAN security practices. The survey process presented in this work is based on a WLAN scanning method called Wardriving. Despite its intimidating name, wardriving is simply a form of passive wireless network scanning. Passive wireless network scanning is used for collecting information about the surrounding wireless networks by listening to the messages broadcasted by wireless network devices. To collect our research data, we conducted wardriving surveys on three separate occasions between the spring of 2019 and early spring of 2020, in a typical medium-sized Finnish city. Our survey results show that 2.2% out of the located networks used insecure encryption protocols and 9.2% of the located networks did not use any encryption protocol. While the percentage of insecure networks is moderately low, we observed during our study that private consumers are reluctant to change the factory-set default settings of their wireless network devices, possibly exposing them to other security threats