@inproceedings{conf/icdm/RingDLH17, added-at = {2023-03-24T00:00:00.000+0100}, author = {Ring, Markus and Dallmann, Alexander and Landes, Dieter and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/233130d87d892cf2f7363ad1fc77486b5/dblp}, booktitle = {ICDM Workshops}, crossref = {conf/icdm/2017w}, editor = {Gottumukkala, Raju and Ning, Xia and Dong, Guozhu and Raghavan, Vijay and Aluru, Srinivas and Karypis, George and Miele, Lucio and Wu, Xindong}, ee = {https://doi.ieeecomputersociety.org/10.1109/ICDMW.2017.93}, interhash = {75a3a48952f20594dc13c34a9e574e1c}, intrahash = {33130d87d892cf2f7363ad1fc77486b5}, isbn = {978-1-5386-3800-2}, keywords = {dblp}, pages = {657-666}, publisher = {IEEE Computer Society}, timestamp = {2024-04-10T08:05:37.000+0200}, title = {IP2Vec: Learning Similarities Between IP Addresses.}, url = {http://dblp.uni-trier.de/db/conf/icdm/icdm2017w.html#RingDLH17}, year = 2017 } @inproceedings{Ring2017, added-at = {2020-10-15T14:36:56.000+0200}, author = {Ring, Markus and Dallmann, Alexander and Landes, Dieter and Hotho, Andreas}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://www.bibsonomy.org/bibtex/2d7d8485b1e1db27d173e2393e3d4c081/annakrause}, booktitle = {2017 {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops 2017, New Orleans, LA, USA, November 18-21, 2017}, doi = {10.1109/ICDMW.2017.93}, editor = {Gottumukkala, Raju and Ning, Xia and Dong, Guozhu and Raghavan, Vijay and Aluru, Srinivas and Karypis, George and Miele, Lucio and Wu, Xindong}, groups = {krause:6}, interhash = {75a3a48952f20594dc13c34a9e574e1c}, intrahash = {d7d8485b1e1db27d173e2393e3d4c081}, keywords = {IP2Vec Representatio RepresentationLearning}, pages = {657--666}, publisher = {{IEEE} Computer Society}, timestamp = {2020-10-15T15:08:52.000+0200}, title = {IP2Vec: Learning Similarities Between {IP} Addresses}, year = 2017 } @inproceedings{ring2017ip2vec, abstract = {IP Addresses are a central part of packet- and flow-based network data. However, visualization and similarity computation of IP Addresses are challenging to due the missing natural order. This paper presents a novel similarity measure IP2Vec for IP Addresses that builds on ideas from Word2Vec, a popular approach in text mining. The key idea is to learn similarities by extracting available context information from network data. IP Addresses are similar if they appear in similar contexts. Thus, IP2Vec is automatically derived from the given network data set. The proposed approach is evaluated experimentally on two public flow-based data sets. In particular, we demonstrate the effectiveness of clustering IP Addresses within a botnet data set. In addition, we use visualization methods to analyse the learned similarities in more detail. These experiments indicate that IP2Vec is well suited to capture the similarity of IP Addresses based on their network communications.}, added-at = {2018-03-14T12:29:50.000+0100}, author = {Ring, Markus and Landes, Dieter and Dallmann, Alexander and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/21c04507d62aed86e0068dc3f27c2efc3/baywiss1}, description = {IP2Vec: Learning Similarities Between IP Addresses - Semantic Scholar}, doi = {10.1109/ICDMW.2017.93}, interhash = {75a3a48952f20594dc13c34a9e574e1c}, intrahash = {1c04507d62aed86e0068dc3f27c2efc3}, isbn = {978-1-5386-3800-2}, issn = {2375-9259}, journal = {2017 IEEE International Conference on Data Mining Workshops (ICDMW)}, keywords = {mr}, pages = {657-666}, timestamp = {2019-03-25T11:52:55.000+0100}, title = {IP2Vec: Learning Similarities Between IP Addresses}, type = {Publication}, year = 2017 } @article{ring2017ip2vec, added-at = {2018-02-02T03:10:10.000+0100}, author = {Ring, Markus and Landes, Dieter and Dallmann, Alexander and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2461b0a31f6321f63f142b2a17a4e5eaf/dmir}, description = {IP2Vec: Learning Similarities Between IP Addresses - Semantic Scholar}, doi = {10.1109/ICDMW.2017.93}, interhash = {75a3a48952f20594dc13c34a9e574e1c}, intrahash = {461b0a31f6321f63f142b2a17a4e5eaf}, isbn = {978-1-5386-3800-2}, issn = {2375-9259}, journal = {2017 IEEE International Conference on Data Mining Workshops (ICDMW)}, keywords = {2017 distance from:hotho ip ip2vec myown security:selected similarity}, pages = {657-666}, timestamp = {2024-01-18T10:31:52.000+0100}, title = {IP2Vec: Learning Similarities Between IP Addresses}, type = {Publication}, year = 2017 } @article{ring2017ip2vec, added-at = {2018-01-31T10:49:58.000+0100}, author = {Ring, Markus and Landes, Dieter and Dallmann, Alexander and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2461b0a31f6321f63f142b2a17a4e5eaf/hotho}, description = {IP2Vec: Learning Similarities Between IP Addresses - Semantic Scholar}, doi = {10.1109/ICDMW.2017.93}, interhash = {75a3a48952f20594dc13c34a9e574e1c}, intrahash = {461b0a31f6321f63f142b2a17a4e5eaf}, isbn = {978-1-5386-3800-2}, issn = {2375-9259}, journal = {2017 IEEE International Conference on Data Mining Workshops (ICDMW)}, keywords = {2017 distance embeddings ip ip2vec myown similarity}, pages = {657-666}, timestamp = {2019-06-30T13:26:33.000+0200}, title = {IP2Vec: Learning Similarities Between IP Addresses}, type = {Publication}, year = 2017 } @inproceedings{ring2017ip2vec, added-at = {2017-10-13T10:23:07.000+0200}, author = {Ring, Markus and Landes, Dieter and Dallmann, Alexander and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/293a598bb15555935abb49168ead0972f/becker}, booktitle = {Proceedings of the Data Mining for Cyber Security Workshop (DMCS) at ICDM}, interhash = {75a3a48952f20594dc13c34a9e574e1c}, intrahash = {93a598bb15555935abb49168ead0972f}, keywords = {auto autoencoder bmbf dmir embedding encoder erp network neural word2vec}, note = {to appear}, timestamp = {2017-10-13T10:23:07.000+0200}, title = {IP2Vec: Learning Similarities between IP Addresses}, year = 2017 }