Secrets Best Not Shared: DNS Privacy Enhancements for the Constrained IoT
- URL: http://arxiv.org/abs/2606.10097v1
- Date: Mon, 08 Jun 2026 19:22:30 GMT
- Title: Secrets Best Not Shared: DNS Privacy Enhancements for the Constrained IoT
- Abstract summary: Attackers often identify DNS traffic to disrupt or compromise Internet services.<n>We consider IETF protocols designed for resource-constrained IoT devices.<n>We show that DNS over CoAP with equalized packet lengths, block-wise transfer, and header compression reduces the accuracy of identifying DNS frames to 86%.
- Score: 2.3195032320229503
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Attackers often identify DNS traffic to disrupt or compromise Internet services. While prior work has focused on encrypting queries using DNS over TLS, HTTPS, or QUIC to counter such attacks, we consider IETF protocols designed for resource-constrained IoT devices and empirically analyze the potential of obfuscating DNS traffic in addition to encryption. We create a dataset of machine-to-machine-compatible data objects along with the corresponding DNS resolution processes, evaluating 296 deployment scenarios of resolving host names, including DNS over the Constrained Application Layer Protocol (CoAP) and an onion routing flavor of CoAP under varying link-layer conditions. We compare them to DNS over HTTPS. Using Random Forest and a header field analysis, we identify fields that leak most information. Our findings show that DNS over CoAP with equalized packet lengths, block-wise transfer, and header compression reduces the accuracy of identifying DNS frames to 86% and further to 77% with payload compression. Our approach outperforms DNS over HTTPS, where classifiers always identify DNS frames based on IP addresses. The dataset is publicly available.
Related papers
- CDN Tsunami: Exploiting HTTP/3-HTTP/1.1 Conversion for DoS Attacks [39.862345027090534]
We study the first study of DoS attacks against HTTP/3 protocols deployed at CDNs.<n>Our key insight is that when the CDN adopts HTTP/3 but the host websites use HTTP/1.1, an adversary can utilize the disparity to amplify a small amount of traffic to the CDN.<n>We conduct a large-scale measurement upon the Tranco Top 1M domain list to quantify the real-world impact of these attacks.
arXiv Detail & Related papers (2026-07-29T08:09:36Z) - WebSentinel: Detecting and Localizing Prompt Injection Attacks for Web Agents [45.87204751555924]
Prompt injection attacks manipulate webpage content to cause web agents to execute attacker-specified tasks instead of the user's intended ones.<n>Existing methods for detecting and localizing such attacks achieve limited effectiveness.<n>We propose WebSentinel, a two-step approach for detecting and localizing prompt injection attacks in webpages.
arXiv Detail & Related papers (2026-02-03T17:55:04Z) - ODoQ: Oblivious DNS-over-QUIC [0.03499870393443268]
Domain Name System (DNS) has advanced enhancements aimed at safeguarding DNS data and users' identity from attackers.<n>The recent privacy-focused advancements have enabled the IETF to standardize several protocols.<n>These protocols tend to focus on either strengthening user privacy (like Oblivious DNS and Oblivious DNS-over-HTTPS) or reducing resolution latency.<n>Our proposed protocol -- 'Oblivious DNS-over-QUIC' (ODoQ) -- leverages the benefits of the QUIC protocol and incorporates an intermediary proxy server to protect the client's identity.
arXiv Detail & Related papers (2025-09-14T06:29:08Z) - Collusion Resistant DNS With Private Information Retrieval [42.34183823376613]
We propose PDNS, a DNS extension leveraging single-server Private Information Retrieval to strengthen privacy guarantees.<n>PDNS achieves acceptable performance (2x faster than DoH over Tor with similar privacy guarantees) and strong privacy guarantees today.
arXiv Detail & Related papers (2025-07-28T13:17:25Z) - Analysis of Robust and Secure DNS Protocols for IoT Devices [8.574167373120648]
We investigate different DNS security approaches using an edge DNS resolver implemented as a Virtual Network Function (VNF)<n>We present our results for cache-based and non-cached responses and evaluate the corresponding security benefits.
arXiv Detail & Related papers (2025-02-13T19:16:39Z) - MTDNS: Moving Target Defense for Resilient DNS Infrastructure [2.8721132391618256]
DNS (Domain Name System) is one of the most critical components of the Internet.
Researchers have been constantly developing methods to detect and defend against the attacks against DNS.
Most solutions discard packets for defensive approaches, which can cause legitimate packets to be dropped.
We propose MTDNS, a resilient MTD-based approach that employs Moving Target Defense techniques.
arXiv Detail & Related papers (2024-10-03T06:47:16Z) - Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models [87.49293964617128]
Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data.<n>We empirically establish that cross-client domain coverage, rather than data heterogeneity, is the pivotal factor.<n>We introduce FedDCA, an algorithm that explicitly maximizes this coverage through diversity-oriented client center selection and retrieval-based augmentation.
arXiv Detail & Related papers (2024-09-30T09:34:31Z) - MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via
Automating Deep Neural Network Porting for Mobile Deployment [54.77943671991863]
MatchNAS is a novel scheme for porting Deep Neural Networks to mobile devices.
We optimise a large network family using both labelled and unlabelled data.
We then automatically search for tailored networks for different hardware platforms.
arXiv Detail & Related papers (2024-02-21T04:43:12Z) - TI-DNS: A Trusted and Incentive DNS Resolution Architecture based on Blockchain [8.38094558878305]
Domain Name System (DNS) is vulnerable to some malicious attacks, including DNS cache poisoning.
This paper presents TI-DNS, a blockchain-based DNS resolution architecture designed to detect and correct the forged DNS records.
TI-DNS is easy to be adopted as it only requires modifications to the resolver side of current DNS infrastructure.
arXiv Detail & Related papers (2023-12-07T08:03:10Z) - A high throughput Intrusion Detection System (IDS) to enhance the security of data transmission among research centers [39.65647745132031]
This paper presents a packet sniffer that was designed using a commercial FPGA development board.
The system can support a data throughput of 10 Gbit/s with preliminary results showing that the speed of data transmission can be reliably extended to 100 Gbit/s.
It is particularly suited for the security of universities and research centers, where point-to-point network connections are dominant.
arXiv Detail & Related papers (2023-11-10T14:30:00Z) - NAS-FAS: Static-Dynamic Central Difference Network Search for Face
Anti-Spoofing [94.89405915373857]
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems.
Existing methods rely on expert-designed networks, which may lead to a sub-optimal solution for task FAS.
Here we propose the first FAS method based on neural search (NAS), called FAS-FAS, to discover the well-suited task-aware networks.
arXiv Detail & Related papers (2020-11-03T23:34:40Z) - Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey [77.99182201815763]
Deep Neural Networks (DNNs) achieve state-of-the-art results in many different problem settings.
DNNs are often treated as black box systems, which complicates their evaluation and validation.
One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations.
arXiv Detail & Related papers (2020-06-30T14:56:05Z) - DSDANet: Deep Siamese Domain Adaptation Convolutional Neural Network for
Cross-domain Change Detection [44.05317423742678]
We propose a novel deep siamese domain adaptation convolutional neural network architecture for cross-domain change detection.
To the best of our knowledge, it is the first time that such a domain adaptation-based deep network is proposed for change detection.
arXiv Detail & Related papers (2020-06-16T15:00:54Z) - DNS Tunneling: A Deep Learning based Lexicographical Detection Approach [1.3701366534590496]
DNS Tunneling is attractive to hackers who exploit it to establish bidirectional communication with machines infected with malware.
The present work proposes a detection approach based on a Convolutional Neural Network (CNN) with a minimal architecture complexity.
Despite its simple architecture, the resulting CNN model correctly detected more than 92% of total Tunneling domains with a false positive rate close to 0.8%.
arXiv Detail & Related papers (2020-06-11T00:10:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.