{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:38:05Z","timestamp":1756309085733,"version":"3.41.0"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T00:00:00Z","timestamp":1548374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2019,1,25]]},"abstract":"<jats:p>Classifying URLs is essential for different applications, such as parental control, URL filtering and Ads\/tracking protection. Such systems historically identify URLs by means of regular expressions, even if machine learning alternatives have been proposed to overcome the time-consuming maintenance of classification rules. Classical machine learning algorithms, however, require large samples of URLs to train the models, covering the diverse classes of URLs (i.e., a ground truth), which somehow limits the applicability of the approach. We here give a first step towards the use of Generative Adversarial Neural Networks (GANs) to classify URLs. GANs are attractive for this problem for two reasons. First, GANs can produce samples of URLs belonging to specific classes even if exposed to a limited training set, outputting both synthetic traces and a robust discriminator. Second, a GAN can be trained to discriminate a class of URLs without being exposed to all other URLs classes - i.e., GANs are robust even if not exposed to uninteresting URL classes during training. Experiments on real data show that not only the generated synthetic traces are somehow realistic, but also the URL classification is accurate with GANs.<\/jats:p>","DOI":"10.1145\/3308897.3308959","type":"journal-article","created":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T14:01:39Z","timestamp":1548684099000},"page":"143-146","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Robust URL Classification With Generative Adversarial Networks"],"prefix":"10.1145","volume":"46","author":[{"given":"Martino","family":"Trevisan","sequence":"first","affiliation":[{"name":"Politecnico di Torino, Torino, Italy"}]},{"given":"Idilio","family":"Drago","sequence":"additional","affiliation":[{"name":"Politecnico di Torino, Torino, Italy"}]}],"member":"320","published-online":{"date-parts":[[2019,1,25]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1486","volume-title":"Advances in neural information processing systems","author":"Denton E. L.","year":"2015","unstructured":"Denton , E. L. , Chintala , S. , Fergus , R. , Deep generative image models using a laplacian pyramid of adversarial networks . In Advances in neural information processing systems ( 2015 ), pp. 1486 -- 1494 . Denton, E. L., Chintala, S., Fergus, R., et al. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in neural information processing systems (2015), pp. 1486--1494."},{"key":"e_1_2_1_2_1","first-page":"2672","volume-title":"Advances in neural information processing systems","author":"Goodfellow I.","year":"2014","unstructured":"Goodfellow , I. , Pouget-Abadie , J. , Mirza , M. , Xu , B. , Warde-Farley , D. , Ozair , S. , Courville , A. , and Bengio , Y . Generative adversarial nets . In Advances in neural information processing systems ( 2014 ), pp. 2672 -- 2680 . Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. In Advances in neural information processing systems (2014), pp. 2672--2680."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1099554.1099649"},{"key":"e_1_2_1_4_1","first-page":"20","volume-title":"Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology","volume":"1","author":"Laurikkala J.","year":"2000","unstructured":"Laurikkala , J. , Juhola , M. , Kentala , E. , Lavrac , N. , Miksch , S. , and Kavsek , B . Informal identification of outliers in medical data . In Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology ( 2000 ), vol. 1 , pp. 20 -- 24 . Laurikkala, J., Juhola, M., Kentala, E., Lavrac, N., Miksch, S., and Kavsek, B. Informal identification of outliers in medical data. In Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (2000), vol. 1, pp. 20--24."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553462"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITC30.2018.00035"},{"key":"e_1_2_1_7_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford A.","year":"2015","unstructured":"Radford , A. , Metz , L. , and Chintala , S . Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ( 2015 ). Radford, A., Metz, L., and Chintala, S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)."},{"key":"e_1_2_1_8_1","first-page":"1","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N.","year":"2014","unstructured":"Srivastava , N. , Hinton , G. , Krizhevsky , A. , Sutskever , I. , and Salakhutdinov , R . Dropout: a simple way to prevent neural networks from overfitting . The Journal of Machine Learning Research 15 , 1 ( 2014 ), 1929--1958. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15, 1 (2014), 1929--1958.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600756CM"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/PST.2014.6890946"},{"key":"e_1_2_1_11_1","volume-title":"Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv preprint","author":"Zhang H.","year":"2017","unstructured":"Zhang , H. , Xu , T. , Li , H. , Zhang , S. , Huang , X. , Wang , X. , and Metaxas , D . Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv preprint ( 2017 ). Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., and Metaxas, D. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv preprint (2017)."}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3308897.3308959","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3308897.3308959","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:58:03Z","timestamp":1750208283000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3308897.3308959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,25]]},"references-count":11,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,1,25]]}},"alternative-id":["10.1145\/3308897.3308959"],"URL":"https:\/\/doi.org\/10.1145\/3308897.3308959","relation":{},"ISSN":["0163-5999"],"issn-type":[{"type":"print","value":"0163-5999"}],"subject":[],"published":{"date-parts":[[2019,1,25]]},"assertion":[{"value":"2019-01-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}