Clock Around the Clock

Sánchez-Rola, Iskander; Santos, Igor; Balzarotti, Davide

doi:10.1145/3243734.3243796

Cited by 53 publications

(9 citation statements)

References 27 publications

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“…Cheng et al [20] relied on the hardware differences between the CPU modules of different devices to detect and identify different devices; Park et al [21] distinguish different devices based on the inherent characteristics of hardware for embedded systems; Sanchez-Rola et al [22] compute a hardware fingerprinting, based on timing the execution of sequences of instructions readily available in API functions.…”

Section: Traditional Detection Methodsmentioning

confidence: 99%

Fine-Grained Identification for Large-Scale IoT Devices: A Smart Probe-Scheduling Approach Based on Information Feedback

et al. 2022

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A large number of IoT devices access the Internet. While enriching our lives, IoT devices bring potential security risks. Device identification is one effective way to mitigate security risks and manage IoT assets. Typical identification algorithms generally separate data capture and target identification into two parts. As a result, it is inefficient and coarse-grained to evaluate the results only once the identification process is complete and then adjust the data capture strategy afterward. To solve this problem, we propose a fine-grained probe-scheduling approach based on information feedback. First, we model the probe surface as three layers for IoT devices and define their relationships. Then, we improve the policy gradient algorithm to optimize the probe policy and generate the optimal probe sequence for the target device. We implement a prototype system and evaluate it on 53,000 IoT devices across various categories to show its wide applicability. The results indicate that our approach can achieve success rates of 96.89%, 93.43%, and 83.71% for device brand, model, and firmware version, respectively, and reduce the identification time by 55.96%.

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Section: Traditional Detection Methodsmentioning

confidence: 99%

Fine-Grained Identification for Large-Scale IoT Devices: A Smart Probe-Scheduling Approach Based on Information Feedback

et al. 2022

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“…And they propose FPSelect, an attribute selection framework which allows users to justify their browser fingerprinting probes for authentication. Sanchez-Rola et al [14] show a new method of computing a hardware fingerprinting, which utilizing time of the execution of sequences of instructions in API functions. Li [15] performed a large-scale measurement for millions of fingerprints to figure out fingerprint dynamics in a real-world website and they found that state-of-the-art fingerprinting tools performs poorly in real-world setting.…”

Section: Related Workmentioning

confidence: 99%

Accurate DNS server fingerprinting based on borderline behavior analysis

Zhang,

Xu,

Shi

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Recent security incidents against the global DNS ecosystem show a close association with outdated versions of DNS servers. Attackers and security researchers widely employ server fingerprinting as a pre-exploit technique. Considering that a single active or passive fingerprinting technique is difficult to achieve accurate and effective results, we proposed a fingerprinting method combining active and passive techniques to identify different DNS servers. We record DNS server fingerprints by collecting interaction borderline behavior of different DNS servers. After that, we propose a novel hierarchical fingerprinting model named as fpdns-ng algorithm. We apply machine learning models like Decision Tree, KNN and Multilayer Perceptron to classify different kinds of DNS servers. Experiment results prove the effectiveness of our method and show that our approach is more accurate than the state-of-the-art in fingerprinting mainstream DNS servers.

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“…Hardware Fingerprints are widely explored on various platforms such as mobile, PC, and IoT devices, to distinguish and track devices [37], [52], [65] or to authenticate devices [36], [26], [56], [24], [21], [22]. Unfortunately, most of these fingerprinting features require special hardware support, such as GPU [37], [52], mobile sensors [65], [32], and NADA flash [60], [24], which are absent in MCU-based embedded devices. For the approaches target IoT devices, HODOR [36] and [26] employ RFID signal features to fingerprint devices which is not a general solution for other kinds of IoT devices.…”

Section: Related Workmentioning

confidence: 99%

From Hardware Fingerprint to Access Token: Enhancing the Authentication on IoT Devices

Xiao,

He,

Zhang

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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The proliferation of consumer IoT products in our daily lives has raised the need for secure device authentication and access control. Unfortunately, these resource-constrained devices typically use token-based authentication, which is vulnerable to token compromise attacks that allow attackers to impersonate the devices and perform malicious operations by stealing the access token. Using hardware fingerprints to secure their authentication is a promising way to mitigate these threats. However, once attackers have stolen some hardware fingerprints (e.g., via MitM attacks), they can bypass the hardware authentication by training a machine learning model to mimic fingerprints or by reusing these fingerprints to craft forged requests.In this paper, we present MCU-Token, a secure hardware fingerprinting framework for MCU-based IoT devices even if the cryptographic mechanisms (e.g., private keys) are compromised. MCU-Token can be easily integrated into various IoT devices by simply adding a short hardware fingerprint-based token to the existing payload. To prevent the reuse of this token, we propose a message mapping approach that binds the token to a specific request by generating the hardware fingerprints based on the request payload. To defeat the machine learning attacks, we mix the valid fingerprints with poisoning data so that attackers cannot train a usable model with the leaked tokens. MCU-Token can defend against adversaries who may replay, craft, and offload the requests via MitM or use both hardware (e.g., use identical devices) and software (e.g., machine learning attacks) strategies to mimic the fingerprints. The system evaluation shows that MCU-Token can achieve high accuracy (over 97%) with low overhead across various IoT devices and application scenarios.

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Clock Around the Clock

Cited by 53 publications

References 27 publications

Fine-Grained Identification for Large-Scale IoT Devices: A Smart Probe-Scheduling Approach Based on Information Feedback

Fine-Grained Identification for Large-Scale IoT Devices: A Smart Probe-Scheduling Approach Based on Information Feedback

Accurate DNS server fingerprinting based on borderline behavior analysis

From Hardware Fingerprint to Access Token: Enhancing the Authentication on IoT Devices

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