Vision Transformer (ViT) has demonstrated promising performance in various computer vision tasks, and recently attracted a lot of research attention. Many recent works have focused on proposing new architectures to improve ViT and deploying it into real-world applications. However, little effort has been made to analyze and understand ViT’s architecture design space and its implication of hardware-cost on different devices. In this work, by simply scaling ViT’s depth, width, input size, and other basic configurations, we show that a scaled vanilla ViT model without bells and whistles can achieve comparable or superior accuracy-efficiency trade-off than most of the latest ViT variants. Specifically, compared to DeiT-Tiny, our scaled model achieves a
Analyzing the Feasibility and Generalizability of Fingerprinting Internet of Things Devices
Abstract In recent years, we have seen rapid growth in the use and adoption of Internet of Things (IoT) devices. However, some loT devices are sensitive in nature, and simply knowing what devices a user owns can have security and privacy implications. Researchers have, therefore, looked at fingerprinting loT devices and their activities from encrypted network traffic. In this paper, we analyze the feasibility of fingerprinting IoT devices and evaluate the robustness of such fingerprinting approach across multiple independent datasets — collected under different settings. We show that not only is it possible to effectively fingerprint 188 loT devices (with over 97% accuracy), but also to do so even with multiple instances of the same make-and-model device. We also analyze the extent to which temporal, spatial and data-collection-methodology differences impact fingerprinting accuracy. Our analysis sheds light on features that are more robust against varying conditions. Lastly, we comprehensively analyze the performance of our approach under an open-world setting and propose ways in which an adversary can enhance their odds of inferring additional information about unseen devices (e.g., similar devices manufactured by the same company).
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- NSF-PAR ID:
- 10314043
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2022
- Issue:
- 2
- ISSN:
- 2299-0984
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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\(\uparrow 1.9\% \) \(\uparrow 3.7\% \) can these scaling strategies be transferred across different real hardware devices ?”; and (2) “can these scaling strategies be transferred to different ViT variants and tasks ?”. For (1), our exploration, based on various devices with different resource budgets, indicates that the transferability effectiveness depends on the underlying device together with its corresponding deployment tool; for (2), we validate the effective transferability of the aforementioned scaling strategies obtained from a vanilla ViT model on top of an image classification task to the PiT model, a strong ViT variant targeting efficiency, as well as object detection and video classification tasks. In particular, when transferred to PiT, our scaling strategies lead to a boosted ImageNet top-1 accuracy of from\(74.6\% \) \(76.7\% \) \(\uparrow 2.1\% \) \(\uparrow 0.7\% \) -
Recent research has shown that in-network observers of WiFi communication (i.e., observers who have joined the WiFi network) can obtain much information regarding the types, user identities, and activities of Internet-of-Things (IoT) devices in the network. What has not been explored is the question of how much information can be inferred by an out-of-network observer who does not have access to the WiFi network. This attack scenario is more realistic and much harder to defend against, thus imposes a real threat to user privacy. In this paper, we investigate privacy leakage derived from an out-of-network traffic eavesdropper on the encrypted WiFi traffic of popular IoT devices. We instrumented a testbed of 12 popular IoT devices and evaluated multiple machine learning methods for fingerprinting and inferring what IoT devices exist in a WiFi network. By only exploiting the WiFi frame header information, we have achieved 95% accuracy in identifying the devices and often their working status. This study demonstrates that information leakage and privacy attack is a real threat for WiFi networks and IoT applications.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2478/popets-2022-0057
