Blockchain-based Object Name Service with Tokenized Authority

Yoon, Wondeuk; Im, Jungho; Choi, Tindal; Kim, Dae-Young

doi:10.1109/tsc.2019.2963027

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…In response to cache poisoning attacks, Li et al 48 proposed a blockchain‐based domain name system, B‐DNS, and quantitatively compared the security of B‐DNS and DNS in terms of attack success rate, attack cost, and attack surface. In response to DNS attacks, Yoon et al 49 proposed a blockchain‐based ONS called BlockONS, which BlockONS consists of content chain extension, data tampering verification method, fault tolerance method, and blockchain token‐based access control method.…”

Section: The Related Workmentioning

confidence: 99%

A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

Wang

Zhang

Xia

et al. 2022

Int J of Intelligent Sys

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Caching and artificial intelligence technology are means to improve the content transmission rate and solve the delay problem in heterogeneous communication networks, which provide an important technical basis for telemedicine diagnosis, disease monitoring, and other medical and healthcare fields. However, since the data used for caching recommendations and mining usually includes a lot of sensitive information, collecting these data will inevitably cause users to worry about personal privacy security. This paper proposes a differential privacy‐preserving deep learning caching framework (DP‐DLCF) for heterogeneous communication networks. First, the multivariate randomized response mechanism is used to perturb the mobile client's sensitive information while introducing the least square method to balance the data privacy and utility, and realize the adaptive allocation of the privacy budget. Then use selective integrated learning methods to further improve the local cache accuracy of the neural network training model. Therefore, the framework not only improves the prediction accuracy of the cache network but also solves the problem of privacy leakage in data sharing. Finally, four simulation examples verify the DP‐DLCF framework's effectiveness.

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Section: The Related Workmentioning

confidence: 99%

A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

Wang

Zhang

Xia

et al. 2022

Int J of Intelligent Sys

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“…actor actor actor (6) α represents the actor learning rate. J θ ( ) takes the average value of N gradients to ensure a more accurate estimation:…”

Section: Components Of Drlmentioning

confidence: 99%

“…Whether the client is a personal computer, intelligent terminal, or virtualized device, 2,3 the DNS cache is needed to accelerate the web access experience. [4][5][6][7] Due to the COVID-19 pandemic, Gartner notes that web clients are expected to reach 6.4 billion by 2022, a 3.2% increase from 2021. 8 Therefore, client-side caching requirements continue to rise.…”

Section: Introductionmentioning

confidence: 99%

“…The client Domain Name System (DNS) cache 1 is a local temporary database of all recently accessed domain names and addresses. Whether the client is a personal computer, intelligent terminal, or virtualized device, 2,3 the DNS cache is needed to accelerate the web access experience 4–7 . Due to the COVID‐19 pandemic, Gartner notes that web clients are expected to reach 6.4 billion by 2022, a 3.2% increase from 2021 8 .…”

Section: Introductionmentioning

confidence: 99%

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An intelligent proactive defense against the client‐side DNS cache poisoning attack via self‐checking deep reinforcement learning

Yang

et al. 2022

Int J of Intelligent Sys

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A new class of poisoning attacks has recently emerged targeting the client-side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current DNS defense architecture does not include DNS clients. Although relative encryption solutions can mitigate this attack, they require the cooperation of multiple parties, and the deployment speed is slow. Therefore, we propose an intelligent-driven proactive defense strategy. First, we model the offensive and defensive process as a stochastic game based on moving target defense. Second, we adopt and optimize Proximal PolicyOptimization (PPO), a deep reinforcement learning method, to solve problems caused by uncertain attack strategies and unknown state transition probability. Third, we design a self-checking component in PPO to solve the uncertainty of action space caused by game state constraints based on our previous work. Thus the convergence speed and stability of PPO are improved. Finally, to the best of our knowledge, we are the first to game with intelligent attackers besides three conventional ones. Our strategy does not require any

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“…Decentralized systems were in principle used to improve the robustness and availability of domain name resolution tasks as well as enabling the feature of by passing censorship campaigns and tampering [6,7,17].…”

Section: Related Workmentioning

confidence: 99%

Computer Science & Information Technology (CS & IT)

2021

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While the Domain Name System (DNS) is an infrastructure of the current network, it still faces the problem of centralization and data authentication according to its concept and practice. Decentralized storage of domain names and user local verification using blockchain may be effective solutions. However, since the blockchain is an add-only type database, domain name changes will cause out of date records to still be correct when using the Simplified Payment Verification (SPV) mechanism locally. This paper mainly introduces Local Enhanced Authentication DNS (LEA-DNS), which allows domain names to be stored in public blockchain database to provide decentralization feature and is compatible with the existing DNS. It achieves the validity and timeliness of local domain name resolution results to ensure correct and up to date with the Merkle Mountain Range and RSA accumulator technologies. Experiments show that less than 3.052Kb is needed for each DNS request to be validated, while the validation time is negligible, and only9.44Kb of data need to be stored locally by the web client. Its compatibility with the existing DNS system and the lightness of the validation protocols indicate that this is a system suitable for deployment widely.

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Blockchain-based Object Name Service with Tokenized Authority

Cited by 10 publications

References 19 publications

A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

An intelligent proactive defense against the client‐side DNS cache poisoning attack via self‐checking deep reinforcement learning

Computer Science & Information Technology (CS & IT)

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