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20172020
most citedKilling Two Birds with One Stone: Malicious Domain Detection with High Accuracy and Coverage

4 citations · 5 across the 3 of their papers we have counts for

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cs.CR2023

TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism

Jiandong Liu, Lan Zhang, Chaojie Lv +3

In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent adversarial parties from colluding to steal othe…

cs.CR2020

Security Analysis of EOSIO Smart Contracts

Ningyu He, Ruiyi Zhang, Lei Wu +5

The EOSIO blockchain, one of the representative Delegated Proof-of-Stake (DPoS) blockchain platforms, has grown rapidly recently. Meanwhile, a number of vulnerabilities and high-pr…

cs.CR20191 cited

DeviceWatch: Identifying Compromised Mobile Devices through Network Traffic Analysis and Graph Inference

Euijin Choo, Mohamed Nabeel, Mashael Alsabah +3

In this paper, we propose to identify compromised mobile devices from a network administrator's point of view. Intuitively, inadvertent users (and thus their devices) who download…

cs.CR2018

Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy

Haipei Sun, Boxiang Dong, Hui +3

Crowdsourcing has arisen as a new problem-solving paradigm for tasks that are difficult for computers but easy for humans. However, since the answers collected from the recruited p…

cs.CR2018

A Survey on Malicious Domains Detection through DNS Data Analysis

Yury Zhauniarovich, Issa Khalil, Ting Yu +1

Malicious domains are one of the major resources required for adversaries to run attacks over the Internet. Due to the important role of the Domain Name System (DNS), extensive res…

cs.CR20174 cited

Killing Two Birds with One Stone: Malicious Domain Detection with High Accuracy and Coverage

Issa Khalil, Bei Guan, Mohamed Nabeel +1

Inference based techniques are one of the major approaches to analyze DNS data and detecting malicious domains. The key idea of inference techniques is to first define associations…