Abstract
In this paper, we present a novel approach to the automated detection of ad-malware. We efficiently crawl a vast set of websites and extensively fetch all HTTP requests embedded in these websites.Then we query these requests both against filtered DNS resolvers and VirusTotal. The idea is to evaluate, how much content is labeled as a potential threat. The results show that up to 8.8% of the domains found in our approach are labeled as suspicious. Moreover, up to 3.2% of these domains are categorized as ad-malware. However, the overall responses from the used services paint a divergent picture: Both DNS resolvers and VirusTotal have different understandings to the definition of suspicious content.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chua, M.Y.K., Yee, G.O.M., Gu, Y.X., Lung, C.H.: Threats to online advertising and countermeasures: a technical survey. Digit. Threats 1(2), 1–27 (2020)
Li, Z., Zhang, K., Xie, Y., Yu, F., Wang, X.: Knowing your enemy: understanding and detecting malicious web advertising. In: the ACM Conference on Computer and Communications Security, vol. 2012, pp. 674–686. ACM (2012)
Nettersheim, F., Arlt, S., Rademacher, M.: Dismantling common internet services for ad-malware detection (2024). https://arxiv.org/abs/2404.14190
Nettersheim, F., Arlt, S., Rademacher, M., Dehling, F.: Katti: an extensive and scalable tool for website analyses. In: Companion Proceedings of the ACM Web Conference 2023, WWW 2023, pp. 217–220. ACM (2023)
Pochat, V.L., van Goethem, T., Tajalizadehkhoob, S., Korczynski, M., Joosen, W.: Tranco: a research-oriented top sites ranking hardened against manipulation. In: 26th Annual Network and Distributed System Security Symposium, 2019. The Internet Society (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nettersheim, F., Arlt, S., Rademacher, M. (2024). Utilizing DNS and VirusTotal for Automated Ad-Malware Detection. In: Stefanidis, K., Systä, K., Matera, M., Heil, S., Kondylakis, H., Quintarelli, E. (eds) Web Engineering. ICWE 2024. Lecture Notes in Computer Science, vol 14629. Springer, Cham. https://doi.org/10.1007/978-3-031-62362-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-62362-2_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-62361-5
Online ISBN: 978-3-031-62362-2
eBook Packages: Computer ScienceComputer Science (R0)