A Denial of Service attack against fair computations using Bitcoin deposits

Beekman, Jethro G.

doi:10.1016/j.ipl.2015.09.009

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Proposition C.1 makes the assumption that clients cannot be denied from communicating with the Bitcoin network during a long enough time period. While DoS attack on clients has been suggested as a possible vulnerability of Bitcoin based protocols [24],…”

Section: Proof Of Security For Accsmentioning

confidence: 99%

Tesseract

Bentov

Zhang

et al. 2019

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

106

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We propose Tesseract, a secure real-time cryptocurrency exchange service. Existing centralized exchange designs are vulnerable to theft of funds, while decentralized exchanges cannot offer real-time cross-chain trades. All currently deployed exchanges are also vulnerable to frontrunning attacks. Tesseract overcomes these flaws and achieves a best-of-both-worlds design by using a trusted execution environment. The task of committing the recent trade data to independent cryptocurrency systems presents an all-or-nothing fairness problem, to which we present ideal theoretical solutions, as well as practical solutions. Tesseract supports not only real-time cross-chain cryptocurrency trades, but also secure tokenization of assets pegged to cryptocurrencies. For instance, Tesseract-tokenized bitcoins can circulate on the Ethereum blockchain for use in smart contracts. We provide a demo implementation of Tesseract that supports Bitcoin, Ethereum, and similar cryptocurrencies.

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Section: Proof Of Security For Accsmentioning

confidence: 99%

Tesseract

Bentov

Zhang

et al. 2019

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

106

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“…They proposed a mechanism that detects DDoS attack in the cloud. In [9] in other to ensure fairness between participants in multi-party, simulated a denial of service attack against two fair multi-party computation.…”

Section: Related Workmentioning

confidence: 99%

Distributed Denial of Service Detection using Multi Layered Feed Forward Artificial Neural Network

Idris¹,

Fabian²,

Abdulhamid³

et al. 2017

IJCNIS

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Abstract-One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.

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