{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SSJYIGW5IC3NSSEHHLA3C533Y3","short_pith_number":"pith:SSJYIGW5","schema_version":"1.0","canonical_sha256":"9493841add40b6d948873ac1b1777bc6dbdcf43f9789b8a69059fcb0dde24831","source":{"kind":"arxiv","id":"1604.02847","version":1},"attestation_state":"computed","paper":{"title":"SymNet: scalable symbolic execution for modern networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Costin Raiciu, Lorina Negreanu, Matei Popovici, Radu Stoenescu","submitted_at":"2016-04-11T09:08:24Z","abstract_excerpt":"We present SymNet, a network static analysis tool based on symbolic execution. SymNet quickly analyzes networks by injecting symbolic packets and tracing their path through the network. Our key novelty is SEFL, a language we designed for network processing that is symbolic-execution friendly.\n  SymNet is easy to use: we have developed parsers that automatically generate SEFL models from router and switch tables, firewall configurations and arbitrary Click modular router configurations. Most of our models are exact and have optimal branching factor. Finally, we built a testing tool that checks "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1604.02847","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2016-04-11T09:08:24Z","cross_cats_sorted":[],"title_canon_sha256":"52bfd1b7807c6d051b2fcf0a952bea66c5cc653fd174cd2c0b44e8c3bda3593b","abstract_canon_sha256":"7e646e417c3744d7d35122c69f38926ae983d9229da3b20f80ab7bd640b0324b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:22.005619Z","signature_b64":"UbNJH66F/77C6vppWY17MOgOdwIo9cuD77QeidCpRqh2O81Q8OiplQMLbHqoiVfA3vnp0rr0m1DZ6heGOAWsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9493841add40b6d948873ac1b1777bc6dbdcf43f9789b8a69059fcb0dde24831","last_reissued_at":"2026-05-18T01:17:22.005043Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:22.005043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SymNet: scalable symbolic execution for modern networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Costin Raiciu, Lorina Negreanu, Matei Popovici, Radu Stoenescu","submitted_at":"2016-04-11T09:08:24Z","abstract_excerpt":"We present SymNet, a network static analysis tool based on symbolic execution. SymNet quickly analyzes networks by injecting symbolic packets and tracing their path through the network. Our key novelty is SEFL, a language we designed for network processing that is symbolic-execution friendly.\n  SymNet is easy to use: we have developed parsers that automatically generate SEFL models from router and switch tables, firewall configurations and arbitrary Click modular router configurations. Most of our models are exact and have optimal branching factor. Finally, we built a testing tool that checks "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.02847","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1604.02847","created_at":"2026-05-18T01:17:22.005127+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.02847v1","created_at":"2026-05-18T01:17:22.005127+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.02847","created_at":"2026-05-18T01:17:22.005127+00:00"},{"alias_kind":"pith_short_12","alias_value":"SSJYIGW5IC3N","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SSJYIGW5IC3NSSEH","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SSJYIGW5","created_at":"2026-05-18T12:30:44.179134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3","json":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3.json","graph_json":"https://pith.science/api/pith-number/SSJYIGW5IC3NSSEHHLA3C533Y3/graph.json","events_json":"https://pith.science/api/pith-number/SSJYIGW5IC3NSSEHHLA3C533Y3/events.json","paper":"https://pith.science/paper/SSJYIGW5"},"agent_actions":{"view_html":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3","download_json":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3.json","view_paper":"https://pith.science/paper/SSJYIGW5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.02847&json=true","fetch_graph":"https://pith.science/api/pith-number/SSJYIGW5IC3NSSEHHLA3C533Y3/graph.json","fetch_events":"https://pith.science/api/pith-number/SSJYIGW5IC3NSSEHHLA3C533Y3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3/action/storage_attestation","attest_author":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3/action/author_attestation","sign_citation":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3/action/citation_signature","submit_replication":"https://pith.science/pith/SSJYIGW5IC3NSSEHHLA3C533Y3/action/replication_record"}},"created_at":"2026-05-18T01:17:22.005127+00:00","updated_at":"2026-05-18T01:17:22.005127+00:00"}