MADONNA: browser-based malicious domain detection through optimized neural network with feature analysis.

Senanayake, Janaka; Rajapaksha, Sampath; Yanai, Naoto; Komiya, Chika; Kalutarage, Harsha

Abstract

The detection of malicious domains often relies on machine learning (ML), and proposals for browser-based detection of malicious domains with high throughput have been put forward in recent years. However, existing methods suffer from limited accuracy. In this paper, we present MADONNA, a novel browser-based detector for malicious domains that surpasses the current state-of-the-art in both accuracy and throughput. Our technical contributions include optimized feature selection through correlation analysis, and the incorporation of various model optimization techniques like pruning and quantization, to enhance MADONNA's throughput while maintaining accuracy. We conducted extensive experiments and found that our optimized architecture, the Shallow Neural Network (SNN), achieved higher accuracy than standard architectures. Furthermore, we developed and evaluated MADONNA's Google Chrome extension, which outperformed existing methods in terms of accuracy and F1-score by six points (achieving 0.94) and four points (achieving 0.92), respectively, while maintaining a higher throughput improvement of 0.87 s. Our evaluation demonstrates that MADONNA is capable of precisely detecting malicious domains, even in real-world deployments.

Citation

SENANAYAKE, J., RAJAPAKSHA, S., YANAI, N., KOMIYA, C. and KALUTARAGE, H. 2024. MADONNA: browser-based malicious domain detection through optimized neural network with feature analysis. In Meyer, N. and Grocholewska-Czuryło, A. (eds.) Revised selected papers from the proceedings of the 38th International conference on ICT systems security and privacy protection (IFIP SEC 2023), 14-16 June 2023, Poznan, Poland. IFIP advances in information and communication technology, 679. Cham: Springer [online], pages 279-292. Available from: https://doi.org/10.1007/978-3-031-56326-3_20

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 38th International conference on ICT systems security and privacy protection (IFIP SEC 2023)
• Start Date: Jun 14, 2023
• End Date: Jun 16, 2023
• Acceptance Date: Apr 15, 2023
• Online Publication Date: Apr 24, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Apr 26, 2024
• Publicly Available Date: Apr 25, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 279-292
• Series Title: IFIP advances in information and communication technology
• Series Number: 679
• Series ISSN: 1868-4238; 1868-422X
• Book Title: Revised selected papers from the proceedings of the 38th International conference on ICT systems security and privacy protection (IFIP SEC 2023)
• ISBN: 9783031563256; 9783031563287
• DOI: https://doi.org/10.1007/978-3-031-56326-3_20
• Keywords: Malicious domain detection; Cybercrime; Systems security; Machine learning; Browser extensions
• Public URL: https://rgu-repository.worktribe.com/output/2308235