CEU Electronic Theses and Dissertations, 2019
| Author | Kerekréty, Matej |
|---|---|
| Title | Detection of malicious domains via a large scale network analysis |
| Summary | In order to protect users from spam, financial scams or malware, security com- panies, such as ESET,1 tend to block dangerous domains and Internet Protocol (IP) addresses. Many of them are chronically known for spreading malware and thus blacklisted, while others are known as clean and whitelisted sources. However, most dangerous domains/IPs are unknown. The aim of this project is to assign a malware probability to domains/IPs using a large scale data on a temporal bipartite network. We model the associated reputation problem as a network interference and graph mining problem, where we construct layers of domains and IP addresses, and seed tthe network with empirical ground truth on malware sources. Then we run the voter model of information spreading to estimate marginal probabilities of domains/IPs being blacklisted. Our analysis provides an intuitive, scalable way of identifying previously unknown, dangerous sources online. |
| Supervisor | Iñiguez, Gerardo |
| Department | Mathematics MSc |
| Full text | https://www.etd.ceu.edu/2019/kerekrety_matej.pdf |
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