{"paper":{"title":"Inferring Networked Device Categories from Low-Level Activity Indicators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Jaideep Chandrashekar, Kyumars Sheykh Esmaili, Pascal Le Guyadec","submitted_at":"2017-09-01T14:47:23Z","abstract_excerpt":"We study the problem of inferring the type of a networked device in a home network by leveraging low level traffic activity indicators seen at commodity home gateways. We analyze a dataset of detailed device network activity obtained from 240 subscriber homes of a large European ISP and extract a number of traffic and spatial fingerprints for individual devices. We develop a two level taxonomy to describe devices onto which we map individual devices using a number of heuristics. We leverage the heuristically derived labels to train classifiers that distinguish device classes based on the traff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00348","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}