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The network traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and IoT manufacturers, and often also shared with their third parties to maintain and enhance user experiences. Unfortunately, on-path adversaries could fingerprint users’ sensitive and private information by analyzing these network traffic traces. While there’s a growing body of literature on defending against this side-channel attack—malicious IoT traffic analytics (TA), there’s currently no systematic method to compare and evaluate the comprehensiveness of these existing studies. To address this problem, we design a new low-cost, open-source system framework—IoT Traffic Exposure Monitoring Toolkit (ITEMTK) that enables people to comprehensively examine and validate prior attack models and their defending approaches. During the design of ITEMTK, we also identified a novel image-based attack capable of inferring sensitive user information, even when users implement the most robust preventative measures in their smart homes. We then present a new, initial, prototype-level defense mechanism—PrivacyProtector, which could effectively defend against a wide range of state-of-the-art machine learning-based TA attacks, including our newly proposed image-based TA attacks. ITEMTK’s flexibility allows users’ easy expansion by integrating new TA attack models and defenses to benchmark their future work.
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Exploring the Unchartered Space of Container Registry Typosquatting
With the increasing popularity of containerized applications, container registries have hosted millions of repositories that allow developers to store, manage, and share their software. Unfortunately, they have also become a hotbed for adversaries to spread malicious images to the public. In this paper, we present the first in-depth study on the vulnerability of container registries to typosquatting attacks, in which adversaries intentionally upload malicious images with an identification similar to that of a benign image so that users may accidentally download malicious images due to typos. We demonstrate that such typosquatting attacks could pose a serious security threat in both public and private registries as well as across multiple platforms. To shed light on the container registry typosquatting threat, we first conduct a measurement study and a 210-day proof-of-concept exploitation on public container registries, revealing that human users indeed make random typos and download unwanted container images. We also systematically investigate attack vectors on private registries and reveal that its naming space is open and could be easily exploited for launching a typosquatting attack. In addition, for a typosquatting attack across multiple platforms, we demonstrate that adversaries can easily self-host malicious registries or exploit existing container registries to manipulate repositories with similar identifications. Finally, we propose CRYSTAL, a lightweight extension to existing image management, which effectively defends against typosquatting attacks from both container users and registries.
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- Award ID(s):
- 2054657
- PAR ID:
- 10412687
- Date Published:
- Journal Name:
- 31st USENIX Security Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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