Botnet Detection Based on DNS Records and Active Probing

Prieto, Iria; Magaña, Eduardo; Morató, D.; Izal, Mikel

doi:10.5220/0003522903070316

Search citation statements

Order By: Relevance

Paper Sections

Select...

Dns-based Features1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

2023

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This results in more A records and more distant NS records in DNS lookup. Moreover, fewer MX records are observed in malicious domains than in benign domains since domains associated with botnets attack usually have no or fewer MX records [15], [16].…”

Section: Dns-based Featuresmentioning

confidence: 99%

Malicious Domain Detection Based on Decision Tree

THEIN,

SHIRAISHI,

MORII

2023

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.

show abstract

Section: Dns-based Featuresmentioning

confidence: 99%