21 research outputs found
Evaluating the Contextual Integrity of Privacy Regulation: Parents' IoT Toy Privacy Norms Versus COPPA
Increased concern about data privacy has prompted new and updated data
protection regulations worldwide. However, there has been no rigorous way to
test whether the practices mandated by these regulations actually align with
the privacy norms of affected populations. Here, we demonstrate that surveys
based on the theory of contextual integrity provide a quantifiable and scalable
method for measuring the conformity of specific regulatory provisions to
privacy norms. We apply this method to the U.S. Children's Online Privacy
Protection Act (COPPA), surveying 195 parents and providing the first data that
COPPA's mandates generally align with parents' privacy expectations for
Internet-connected "smart" children's toys. Nevertheless, variations in the
acceptability of data collection across specific smart toys, information types,
parent ages, and other conditions emphasize the importance of detailed
contextual factors to privacy norms, which may not be adequately captured by
COPPA.Comment: 18 pages, 1 table, 4 figures, 2 appendice
Machine Learning DDoS Detection for Consumer Internet of Things Devices
An increasing number of Internet of Things (IoT) devices are connecting to
the Internet, yet many of these devices are fundamentally insecure, exposing
the Internet to a variety of attacks. Botnets such as Mirai have used insecure
consumer IoT devices to conduct distributed denial of service (DDoS) attacks on
critical Internet infrastructure. This motivates the development of new
techniques to automatically detect consumer IoT attack traffic. In this paper,
we demonstrate that using IoT-specific network behaviors (e.g. limited number
of endpoints and regular time intervals between packets) to inform feature
selection can result in high accuracy DDoS detection in IoT network traffic
with a variety of machine learning algorithms, including neural networks. These
results indicate that home gateway routers or other network middleboxes could
automatically detect local IoT device sources of DDoS attacks using low-cost
machine learning algorithms and traffic data that is flow-based and
protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep
Learning and Security (DLS '18
A Developer-Friendly Library for Smart Home IoT Privacy-Preserving Traffic Obfuscation
The number and variety of Internet-connected devices have grown enormously in
the past few years, presenting new challenges to security and privacy. Research
has shown that network adversaries can use traffic rate metadata from consumer
IoT devices to infer sensitive user activities. Shaping traffic flows to fit
distributions independent of user activities can protect privacy, but this
approach has seen little adoption due to required developer effort and overhead
bandwidth costs. Here, we present a Python library for IoT developers to easily
integrate privacy-preserving traffic shaping into their products. The library
replaces standard networking functions with versions that automatically
obfuscate device traffic patterns through a combination of payload padding,
fragmentation, and randomized cover traffic. Our library successfully preserves
user privacy and requires approximately 4 KB/s overhead bandwidth for IoT
devices with low send rates or high latency tolerances. This overhead is
reasonable given normal Internet speeds in American homes and is an improvement
on the bandwidth requirements of existing solutions.Comment: 6 pages, 6 figure
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