New references

bibliography.bib

@@ -143,4 +143,33 @@
   file = {Andriesse et al. - 2015 - Reliable Recon in Adversarial Peer-to-Peer Botnets.pdf:/home/me/Zotero/storage/YJZMYTCB/Andriesse et al. - 2015 - Reliable Recon in Adversarial Peer-to-Peer Botnets.pdf:application/pdf}
 }
 
+@inproceedings{karuppayah_sensorbuster_2017,
+  title = {{{SensorBuster}}: {{On Identifying Sensor Nodes}} in {{P2P Botnets}}},
+  shorttitle = {{{SensorBuster}}},
+  booktitle = {Proceedings of the 12th {{International Conference}} on {{Availability}}, {{Reliability}} and {{Security}}},
+  author = {Karuppayah, Shankar and Böck, Leon and Grube, Tim and Manickam, Selvakumar and Mühlhäuser, Max and Fischer, Mathias},
+  date = {2017-08-29},
+  pages = {1--6},
+  publisher = {{Association for Computing Machinery}},
+  location = {{New York, NY, USA}},
+  doi = {10.1145/3098954.3098991},
+  url = {https://doi.org/10.1145/3098954.3098991},
+  urldate = {2021-03-23},
+  abstract = {The ever-growing number of cyber attacks originating from botnets has made them one of the biggest threat to the Internet ecosystem. Especially P2P-based botnets like ZeroAccess and Sality require special attention as they have been proven to be very resilient against takedown attempts. To identify weaknesses and to prepare takedowns more carefully it is thus a necessity to monitor them by crawling and deploying sensor nodes. This in turn provokes botmasters to come up with monitoring countermeasures to protect their assets. Most existing anti-monitoring countermeasures focus mainly on the detection of crawlers and not on the detection of sensors deployed in a botnet. In this paper, we propose two sensor detection mechanisms called SensorRanker and SensorBuster. We evaluate these mechanisms in two real world botnets, Sality and ZeroAccess. Our results indicate that SensorRanker and SensorBuster are able to detect up to 17 sensors deployed in Sality and four within ZeroAccess.},
+  file = {/home/me/Zotero/storage/ZDUFTXYY/Karuppayah et al. - 2017 - SensorBuster On Identifying Sensor Nodes in P2P B.pdf},
+  isbn = {978-1-4503-5257-4},
+  keywords = {Anti-monitoring,Countermeasure,Detection,P2P Botnet,Sensor},
+  series = {{{ARES}} '17}
+}
+
+@report{page_pagerank_1998,
+  title = {{The PageRank Citation Ranking: Bringing Order to the Web}},
+  shorttitle = {{The PageRank Citation Ranking}},
+  author = {Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry},
+  date = {1998-01-29},
+  url = {http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf},
+  urldate = {2021-11-30},
+  abstract = {The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describ es PageRank, a method for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.}
+}
+
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