Re-measuring the label dynamics of online anti-malware engines from millions of samples

J Wang, L Wang, F Dong, H Wang - Proceedings of the 2023 ACM on …, 2023 - dl.acm.org
VirusTotal is the most widely used online scanning service in both academia and industry.
However, it is known that the results returned by antivirus engines are often inconsistent and …

Measuring and modeling the label dynamics of online {Anti-Malware} engines

S Zhu, J Shi, L Yang, B Qin, Z Zhang, L Song… - 29th USENIX Security …, 2020 - usenix.org
VirusTotal provides malware labels from a large set of anti-malware engines, and is heavily
used by researchers for malware annotation and system evaluation. Since different engines …

Benchmarking label dynamics of virustotal engines

S Zhu, Z Zhang, L Yang, L Song, G Wang - Proceedings of the 2020 …, 2020 - dl.acm.org
VirusTotal is the largest online anti-malware scanning service. It is widely used by security
researchers for labeling malware data or serving as a comparison baseline. However …

View from above: exploring the malware ecosystem from the upper DNS hierarchy

A Faulkenberry, A Avgetidis, Z Ma, O Alrawi… - Proceedings of the 38th …, 2022 - dl.acm.org
This work explores authoritative DNS (AuthDNS) as a new measurement perspective for
studying the large-scale epidemiology of the malware ecosystem—when and where …

A lustrum of malware network communication: Evolution and insights

C Lever, P Kotzias, D Balzarotti… - … IEEE Symposium on …, 2017 - ieeexplore.ieee.org
Both the operational and academic security communities have used dynamic analysis
sandboxes to execute malware samples for roughly a decade. Network information derived …

Towards identifying early indicators of a malware infection

S Karapoola, C Rebeiro, U Parekh… - Proceedings of the 2019 …, 2019 - dl.acm.org
A malware goes through multiple stages in its life-cycle at the target machine before
mounting its expected attack. The entire life-cycle can span anywhere from a few weeks to …

Improving zero-day malware testing methodology using statistically significant time-lagged test samples

K Berlin, J Saxe - arXiv preprint arXiv:1608.00669, 2016 - arxiv.org
Enterprise networks are in constant danger of being breached by cyber-attackers, but
making the decision about what security tools to deploy to mitigate this risk requires carefully …

Investigating labelless drift adaptation for malware detection

Z Kan, F Pendlebury, F Pierazzi… - Proceedings of the 14th …, 2021 - dl.acm.org
The evolution of malware has long plagued machine learning-based detection systems, as
malware authors develop innovative strategies to evade detection and chase profits. This …

Waves of malice: A longitudinal measurement of the malicious file delivery ecosystem on the web

CC Ife, Y Shen, SJ Murdoch, G Stringhini - Proceedings of the 2019 ACM …, 2019 - dl.acm.org
We present a longitudinal measurement of malicious file distribution on the Web. Following
a data-driven approach, we identify network infrastructures and the files that they download …

The dropper effect: Insights into malware distribution with downloader graph analytics

BJ Kwon, J Mondal, J Jang, L Bilge… - Proceedings of the 22nd …, 2015 - dl.acm.org
Malware remains an important security threat, as miscreants continue to deliver a variety of
malicious programs to hosts around the world. At the heart of all the malware delivery …