What type of study is this?

This is a Human Clinical Trial study.

September 10, 2025

Host‐Level Botnet Detection via Internet DNS Traffic Analysis Using Neural Networks

Key Points

The model achieved a detection accuracy of 99.22%, effectively distinguishing botnet traffic from legitimate queries.
Comparative analysis shows a lower misclassification rate compared to existing detection methods.
Deep learning techniques, particularly Bidirectional Gated Recurrent Units, capture temporal patterns in DNS sequences.
The proposed approach was trained and tested on the UNSW-NB15 dataset, validating its effectiveness.

Abstract

ABSTRACT Botnets remain one of the most significant threats in Internet security, performing large‐scale attacks such as distributed denial of service (DDoS), data exfiltration, and financial fraud. Detecting botnet activity at the host level is crucial for early mitigation, particularly by analyzing anomalies in domain name system (DNS) query sequences. This study proposes a deep learning‐based DNS sequence analysis that leverages Bidirectional Gated Recurrent Units (BiGRU) to identify deviations in DNS query behavior indicative of botnet activity. The model learns temporal patterns in DNS sequences, distinguishing legitimate traffic from botnet‐generated queries by capturing contextual dependencies over time. The proposed approach is trained and evaluated on a UNSW‐NB15 dataset. The performance assessment of the proposed model demonstrates its effectiveness in detecting botnets with an accuracy of 99.22%. The comparative analysis with the existing approaches highlights the improvements in detection accuracy with a low misclassification rate.

Demander à l'IA

Bookmark