{"as_of":"2026-08-16T21:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:60a3310b7fad0465d8aaa136c8b7e88b447ebe942ea8b7b6de212d5b9d65352e","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:11:19.447162Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.03640/citation-record","integrity":"/paper/1908.03640/integrity","json":"/paper/1908.03640/citation-record.json","paper":"/paper/1908.03640"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.693041Z","title":", González, F.A.: Classify- ing phishing urls using recurrent neural networks","venue":null,"work_id":"a9e26145-961b-4b46-a25a-38042d44143a","year":2017},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.363412Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:2509449a67280465daa05dacdb27feed2026c50f4bd8a81553c04be0d9e521e4","observation_id":"093ef1bc-d9f7-4dc2-91b3-7ab9b545478a","resolution":{"observed_at":"2026-08-14T14:11:19.696183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.684611Z","title":"IEEE Transactions on Neural Networks 5(2), 157–166 (March 1994)","venue":null,"work_id":"7dec25a0-9c70-4338-a3a2-0eb1fa9cc754","year":1994},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.367121Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:950ae898a90b61c1ad6f21dc2f0018a51ae498714b110b6c2df2f5dd429dfc22","observation_id":"53a31ca0-925c-42d8-b02e-d1f8415873e7","resolution":{"observed_at":"2026-08-14T14:11:19.687759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.676448Z","title":"In: Proceedings of t he Fifth Conference on Email and Anti-Spam","venue":null,"work_id":"4d9856a5-6bb4-43fa-bfba-104e8aee6149","year":2008},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.370189Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:2f9378539394f917849396f6c985ea1d06b800c61234272914fe39f48124bdea","observation_id":"e2babc4e-d266-4c5d-82b5-d9d45b9197d2","resolution":{"observed_at":"2026-08-14T14:11:19.679468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.668115Z","title":", Strobel, S.: New filtering approaches for phishing email","venue":null,"work_id":"daaf48f2-0339-4421-9315-95d3b4ab3e7a","year":2010},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.373436Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:e750095e59d121ff6a1d00178b7c53271b037868cb3588b95d3a3df421bed7e4","observation_id":"cb32d628-b326-401d-8854-6dc1d28fba38","resolution":{"observed_at":"2026-08-14T14:11:19.671213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.659785Z","title":"In: Proceedings of the 9th A nnual NYS Cyber Security Conference","venue":null,"work_id":"0432a6b4-2b34-4002-b457-35d48ddc487c","year":2006},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.376823Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:9926c507f9103befa44760b44882423a1bb8fb0f4a0b1e857f4cdae4e06c66a6","observation_id":"71868313-8192-40b5-9027-06eafe4d17a0","resolution":{"observed_at":"2026-08-14T14:11:19.662741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.650464Z","title":"In: Proceed- ings of the 16th international conference on World Wide Web","venue":null,"work_id":"cea4e95a-7681-448f-87a6-0cb0b82026d0","year":2007},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.379776Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:d1754040ac7a1ddf36cf4c20b82f4882acf56e5d93f3b45831f2acaacc2f0fe8","observation_id":"e60fcdcb-9fb4-4a83-b9cd-9eb20c4418e5","resolution":{"observed_at":"2026-08-14T14:11:19.654653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.642085Z","title":"Neural Computation 12(10), 2451–2471 (October 2000)","venue":null,"work_id":"408ecf12-0cb5-4593-889a-8f8be10a87f8","year":2000},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.382955Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:ccc217990461114d9e7786585e5d693d508278a1149823917dc461745d8aca61","observation_id":"5a6c0323-772b-47f2-8684-ee68d86bbf18","resolution":{"observed_at":"2026-08-14T14:11:19.645187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.634146Z","title":"MIT Press, Cambridge, MA, USA (2016)","venue":null,"work_id":"cbce3bd0-f54a-4ea0-8f80-7146be9ef4da","year":2016},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.386301Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:b9f7de4c3b57b65b4f3153031cc686e1a9dfe820959fe472636a6bf3722bff8b","observation_id":"fb0437c3-91ad-4b6a-abda-7b7c7153bc91","resolution":{"observed_at":"2026-08-14T14:11:19.637031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.625422Z","title":"Neural Computation 9(8), 1735–1780 (November 1997)","venue":null,"work_id":"df74c2fa-b232-49e2-be31-4d783e13afb9","year":1997},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.389124Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:00fbc9fa13dae2fd2d565284f3e1cbe55ea059971f1ff0dcf788d3733fe04232","observation_id":"d5d80886-4f5b-40bd-96c7-e4687f4d7f7e","resolution":{"observed_at":"2026-08-14T14:11:19.629009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.616571Z","title":"Human-centric Computing and In formation Sciences 6 (June 2016)","venue":null,"work_id":"a06775fe-6ba1-4e78-a73d-8e93c8b36483","year":2016},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.391972Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:d817fd691998be70a7a0181e7acc830183bbd332151fd3d61d732c6c78eb5eee","observation_id":"f4c199e0-9b44-4b2c-ad85-bd3ae68bd237","resolution":{"observed_at":"2026-08-14T14:11:19.619630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.607962Z","title":"In: Proceedings of the 32nd Interna tional Conference on Machine Learning","venue":null,"work_id":"0277d225-d248-4e45-abdf-7d410c7753a1","year":2015},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.394820Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:103f08a51264439952188d5759d647c6ad13eba5a13aa80d95566f11214a4fb6","observation_id":"b5fdf30e-eede-4c71-bf57-85ecc23eff81","resolution":{"observed_at":"2026-08-14T14:11:19.610968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.598959Z","title":"IEEE Communications Surveys & Tutorials 15(4), 2091–2121 (April 2013)","venue":null,"work_id":"7cae7e4e-8457-48ff-8f85-ebbfd01f02ce","year":2013},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.397735Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:61cbc0b22f179615ba7675349c0e427016d96f8cf48a41cbfef471a1b0c0f9a9","observation_id":"c88a05cb-7800-4cde-84f3-d6d54ba602b4","resolution":{"observed_at":"2026-08-14T14:11:19.601960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.589840Z","title":"Neural Computing and A pplications 25(2), 443– 458 (August 2014)","venue":null,"work_id":"9853e907-868f-4d46-8f47-b57a13ed174a","year":2014},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.404392Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:2536f384dd8a14c2d63f24709342443b39e97fb61d81f9a6ba7d54b3e16af0f3","observation_id":"964c5098-4610-4bb1-9c27-e1f8d7a4553c","resolution":{"observed_at":"2026-08-14T14:11:19.593723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.580676Z","title":null,"venue":null,"work_id":"abcffcdf-4879-4028-9fca-b2377f4ea1db","year":null},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.407228Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:e7f4bdaa34eddbd5432bf7bcc92ad66b4a10755988a82b679bc1f244c420b990","observation_id":"bb7e3bf9-9857-4c76-b505-6474914c832e","resolution":{"observed_at":"2026-08-14T14:11:19.584641Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.572501Z","title":"In: The Oxford Handbook of Cyberps ychology","venue":null,"work_id":"56eb7950-4a60-47fb-8ee0-5acd787ab6d9","year":2019},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.410269Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:7a3c637af0cb84ffeb0c2228670c3410b9c5c1c745f9904d05e2362fd11e9c69","observation_id":"98d1b973-52f2-49cd-9f1a-9cdcf74cf4e9","resolution":{"observed_at":"2026-08-14T14:11:19.575430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.563943Z","title":"In: Proceedings of the 30th International Con ference on Machine Learning","venue":null,"work_id":"790ec81d-e21d-4373-a121-402614569743","year":2013},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.413963Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:3c5ca94a7ec6131f1304aaeee4c00a70c2a71205baf4b29e928a647c584635d5","observation_id":"e2276262-5a36-4a4c-aec5-e8c7fc3f9594","resolution":{"observed_at":"2026-08-14T14:11:19.567013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.555344Z","title":null,"venue":null,"work_id":"14db09e0-c3bb-43c6-bc19-5a9ccc1745e3","year":2016},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.416709Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:561047f24b0200aad8efcbe4d3460ddf871458e8d5ca52abb5cdf15883314894","observation_id":"fc54c0bd-f8da-4311-9b74-159890866384","resolution":{"observed_at":"2026-08-14T14:11:19.558646Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.546789Z","title":null,"venue":null,"work_id":"0f57d0fe-e3b8-43a9-afbf-b4be7981d816","year":null},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.420396Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:abed36d2d932c198cad905ab7f695fac324df459f88f891e1dbb9420b97cf60c","observation_id":"80919bff-f59b-46ba-8240-48586a546ed6","resolution":{"observed_at":"2026-08-14T14:11:19.549742Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.538036Z","title":"Hand book of Information and Communication Security pp","venue":null,"work_id":"68052c5f-e080-40df-b5e0-d78af5aa5d7c","year":2010},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.423441Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:2e5adbe6c237094a9b8d177a38a517ad2d0845ec8fc971b927e207ecf6754937","observation_id":"c79aab83-c049-4b10-b43a-b677f9f2a3eb","resolution":{"observed_at":"2026-08-14T14:11:19.541297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6120","last_updated":"2014-02-19T17:26:57Z","snapshot_observed_at":"2026-08-14T23:50:45.351114Z","submitted_at":"2013-12-20T20:24:00Z","title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6120","snapshot_observed_at":"2026-08-14T14:11:19.426222Z","title":"arXiv pre print arXiv:1312.6120 (2013)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.426222Z"},"links":{"cited_paper":"/paper/1312.6120","citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:5436922ffd13ff8b2a5eb6221f11c3933f7f46034c5310a2180366aafe55b98c","observation_id":"9a55f54f-23af-4036-ad83-0ab2c404171e","resolution":{"observed_at":"2026-08-14T14:11:19.426222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.529100Z","title":null,"venue":null,"work_id":"7d9d8a78-e93b-45b4-8141-09db75cdad74","year":null},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.429195Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:513e0ff42710def72ca426ce80ff9db8a368181b56c48d140fef87903d44b039","observation_id":"7fe9e552-5086-4f8c-8387-4095c55b261a","resolution":{"observed_at":"2026-08-14T14:11:19.532019Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.519335Z","title":"Journal of Machine Learning Research 15(1), 1929–1958 (January 2014)","venue":null,"work_id":"35796662-1d44-46d1-a72c-89b837491ca2","year":1929},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.432088Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:0e855a078be3faf9e44e4711c3f680332440e0af0719e844a626a7626851f79a","observation_id":"b09bb8be-3b09-4d11-9dad-f2f222794fe0","resolution":{"observed_at":"2026-08-14T14:11:19.522540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.510555Z","title":"In: 2010 eCrime Researchers Summit","venue":null,"work_id":"2d3ca7ca-7795-4fd6-9380-613aa034e2ba","year":2010},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.434929Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:48a2065a319c8a3f6fa544c4b5d31ea7d7c1cbca6189f0fdb768cd4a5cb49a97","observation_id":"0f8aed37-4c7d-4319-9c40-04ef67009ce8","resolution":{"observed_at":"2026-08-14T14:11:19.513555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.501336Z","title":"In: 17th European Symposium on Research in Com puter Security","venue":null,"work_id":"83b02180-2f2b-4527-951d-cce3ac64abae","year":2012},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.437763Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:a530dbae1f30c1c2c34401f2f077f1a06edd27d9f28da3355d2e4e270c3526b2","observation_id":"742e884a-dad6-4311-bfdc-104c42fd1636","resolution":{"observed_at":"2026-08-14T14:11:19.504467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.491801Z","title":"Jour nal of Intelligent & Fuzzy Systems 34(3), 1333–1343 (March 2018)","venue":null,"work_id":"d388a1d0-6c77-4cb0-a70c-0aeb81dac5f9","year":2018},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.440892Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:a3162d890664e0e34c61ac21acab292c94050bc43b8ac5b2784c8c5156e847ab","observation_id":"14bb585f-06cb-4d77-900d-b997970afb24","resolution":{"observed_at":"2026-08-14T14:11:19.495623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.2329","last_updated":"2015-02-19T14:46:00Z","snapshot_observed_at":"2026-08-14T23:20:21.606348Z","submitted_at":"2014-09-08T13:08:00Z","title":"Recurrent Neural Network Regularization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.2329","snapshot_observed_at":"2026-08-14T14:11:19.443768Z","title":"arXiv preprint arXiv:1409.2329 (2014) Catching the Phish 13","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.443768Z"},"links":{"cited_paper":"/paper/1409.2329","citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:7083f5cf9e772860c7497ad4d2d62b9698198affb826b519dc31c123f6307888","observation_id":"3f23f854-b771-4b43-91f3-2b7e219695ac","resolution":{"observed_at":"2026-08-14T14:11:19.443768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:11:19.481700Z","title":"In: Proceedings of the 12th Inte rnational Conference on Innovative Mobile and Internet Services in Ubiquitous Co mputing","venue":null,"work_id":"755d498b-3efd-44ec-b002-61fc8e298a64","year":2018},"citing_paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:11:19.447162Z"},"links":{"citing_paper":"/paper/1908.03640"},"observation_digest":"sha256:d93230e77d557b848d3bfa7e4f2a908f50dd47cdc5495749a244b3c3e4144be1","observation_id":"1c2c750e-f56d-43ec-af79-f20fe82053e5","resolution":{"observed_at":"2026-08-14T14:11:19.486022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.03640","last_updated":"2019-08-09T21:37:42Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-16T14:20:11.991667Z","submitted_at":"2019-08-09T21:37:42Z","title":"Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":21},"total_outbound_references":27},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:1908.03640."}