Domain name system security and privacy: A contemporary survey

Khormali, Aminollah; Park, Jeman; Alasmary, Hisham; Anwar, Afsah; Saad, Muhammad; Mohaisen, Aziz

doi:10.1016/j.comnet.2020.107699

Cited by 30 publications

(10 citation statements)

References 109 publications

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“…Although DNS is a critical protocol, it was not designed with security and privacy in mind. DNS is vulnerable to amplification attacks, DNS cache poisoning, botnet attacks, phishing attacks, and DNS manipulation [5]. DNS-over-Encryption protocols such as DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT) were introduced to address these security concerns [2].…”

Section: Figure 1 the Dns Protocolmentioning

confidence: 99%

Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol

Behnke¹,

Briner

Cullen

et al. 2021

IEEE Access

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This research project was supported by the state of Colorado through funds appropriated for cybersecurity law dubbed "Cyber Coding Cryptology for State Records." Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding sources. Authors would like to thank Clayton P. Johnson and Bishal Khadka for their early help with getting this research project started.

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Section: Figure 1 the Dns Protocolmentioning

confidence: 99%

Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol

Behnke¹,

Briner

Cullen

et al. 2021

IEEE Access

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“…(3) Using the IoT-Botnet 2020 [37] dataset and analyzing its effectiveness, we intended to evaluate the efficiency of our model with other deep learning models based on different Logit-boosted techniques (4) This study is aimed at seeing how numerical and categorical factors affected the performance of Logitboosted network-based intrusion detection system models…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Hameed

Yang

Ghafoor

et al. 2022

Wireless Communications and Mobile Computing

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In the Internet of Things (IoT) era, the mobile crowd sensing system (MCS) has become increasingly important. The Internet of Things Auto (IOTA) has evolved rapidly in practically every technology field over the last decade. IOTA-based mobile crowd sensing technology is being developed in this study using machine learning to detect and prevent mobile users from engaging in fake sensing activities. It has been determined through testing and evaluation that our method is effective for both quality estimation and incentive allocation. Using the IOTA Bottleneck dataset, multiple performance metrics were used to demonstrate how well logit-boosted algorithms perform. After applying logit-boosted algorithms on the dataset for the classification, Logi-XGB scored 95.7 percent accuracy, while Logi-GBC scored 90.8 percent accuracy. As a result of this, Logi-ABC had an accuracy rate of 89%. Logi-CBC, on the other hand, got the highest accuracy of 99.8%. Logi-LGBM and Logi-HGBC both scored 91.37 percent accuracy, which is identical. On the given dataset, our Logi-CBC algorithm outperforms earlier Logit-boosted algorithms in terms of accuracy. Using the new IoTA-Botnet 2020 dataset, a new proposed methodology is tested. In comparison to prior Logit-boosted algorithms, the new model Logi-CBC has a highest detection accuracy of 99.8%.

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“…Several studies characterize the use of response manipulation in resolvers [13,15,24], including both filtering, censoring, injection, etc. Most papers consider response manipulation as an undesired feature as opposed to something positive that the user has actively chosen to gain features such as malware protection.…”

Section: Dns Response Manipulationmentioning

confidence: 99%

A Study on the Use of 3rd Party DNS Resolvers for Malware Filtering or Censorship Circumvention

Fejrskov¹,

Vasilomanolakis²,

Pedersen³

2022

ICT Systems Security and Privacy Protection

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DNS resolvers perform the essential role of translating domain names into IP addresses. The default DNS resolver offered by an Internet Service Provider (ISP) can be undesirable for a number of reasons such as censorship, lack of malware filtering options and low service quality. In this paper, we propose a novel method for estimating the amount of DNS traffic directed at non-ISP resolvers by using DNS and NetFlow data from an ISP. This method is extended to also estimate the amount of DNS traffic towards resolvers that offer malware filtering or parental control functionality. Finally, we propose a novel method for estimating the amount of DNS traffic at non-ISP resolvers that would have been censored by ISP resolvers. The results of applying these methods on an ISP dataset shows to which extent 3rd party resolvers are chosen by users for either malware filtering or censorship circumvention purposes.

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Domain name system security and privacy: A contemporary survey

Cited by 30 publications

References 109 publications

Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol

Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

A Study on the Use of 3rd Party DNS Resolvers for Malware Filtering or Censorship Circumvention

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