2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) 2020
DOI: 10.1109/ccece47787.2020.9255697
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Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection

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Cited by 20 publications
(14 citation statements)
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“…In Lightweight Directory Access Protocol (LDAP) DDoS attack [8], the attacker sends an LDAP request to an LDAP server to produce large replies, with a spoofed sender IP address. Domain Name System (DNS) [9] amplification is a reflection-based DDoS attack, which manipulates domain name systems and makes them flood the target system with large quantities of UDP packets, which bring down the target servers.…”
Section: Reflection-based Ddos Attacksmentioning
confidence: 99%
“…In Lightweight Directory Access Protocol (LDAP) DDoS attack [8], the attacker sends an LDAP request to an LDAP server to produce large replies, with a spoofed sender IP address. Domain Name System (DNS) [9] amplification is a reflection-based DDoS attack, which manipulates domain name systems and makes them flood the target system with large quantities of UDP packets, which bring down the target servers.…”
Section: Reflection-based Ddos Attacksmentioning
confidence: 99%
“…Within the context of DNS, multiple researchers have proposed the use of ML models for malicious DNS queries detection [23]- [28]. For example, Moubayed et al proposed the use of exploratory data analytics, ensemble feature selection models, and ensemble classification models to understand the characteristics of different DNS typo-squatting features and accurately detect malicious URLs respectively [23,24]. On the other hand, Sivakorn et al proposed using deep neural network models to detect malicious DNS queries [25].…”
Section: Related Workmentioning
confidence: 99%
“…Related Work ML classification techniques have been proposed as part of various network attack detection frameworks and other applications using different classification models such as Support Vector Machines (SVM) [15], Decision Trees [16], KNN [17], Artificial Neural Networks (ANN) [18], [19], and Naive Bayes [20] as illustrated in [1]. One such application is the DNS typo-squatting attack detection framework presented in [21], [22]. Also, ML techniques have been proposed to detect zero-day attacks as illustrated by the probabilistic Bayesian network model presented in [23].…”
Section: Related Work and Limitationsmentioning
confidence: 99%