{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/intrusion-detection/papers/ran/1","list_of":"/task/intrusion-detection","task":"Intrusion Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,14],"of":14,"counts":{"archive_papers_tagged":800,"with_a_code_link":151,"where_syntology_ran_a_sample":14,"not_listed_spam_title":0,"listed":800,"listed_where_code_ran":14,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":11,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":11,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/intrusion-detection/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/pyod-2-a-python-library-for-outlier-detection","slug":"pyod-2-a-python-library-for-outlier-detection","title":"PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection","date":"2024-12-11","arxiv_id":"2412.12154","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pyod-2-a-python-library-for-outlier-detection#ran","syntology_url":"https://syntology.ai/paper/2412.12154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12154"}},"official":{"repos":["yzhao062/pyod"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/data-distribution-valuation","slug":"data-distribution-valuation","title":"Data Distribution Valuation","date":"2024-10-06","arxiv_id":"2410.04386","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/data-distribution-valuation#ran","syntology_url":"https://syntology.ai/paper/2410.04386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04386"}},"official":{"repos":["xinyiys/data_distribution_valuation"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/large-language-models-for-cyber-security-a","slug":"large-language-models-for-cyber-security-a","title":"Large Language Models for Cyber Security: A Systematic Literature Review","date":"2024-05-08","arxiv_id":"2405.04760","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/large-language-models-for-cyber-security-a#ran","syntology_url":"https://syntology.ai/paper/2405.04760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04760"}},"official":{"repos":["hiyouga/llama-efficient-tuning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/kairos-practical-intrusion-detection-and","slug":"kairos-practical-intrusion-detection-and","title":"Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance","date":"2023-08-09","arxiv_id":"2308.05034","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":12,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/kairos-practical-intrusion-detection-and#ran","syntology_url":"https://syntology.ai/paper/2308.05034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05034"}},"official":{"repos":["provenanceanalytics/kairos"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-reliable-rare-category-analysis-on","slug":"towards-reliable-rare-category-analysis-on","title":"Towards Reliable Rare Category Analysis on Graphs via Individual Calibration","date":"2023-07-19","arxiv_id":"2307.09858","repositories_listed":1,"syntology":{"n":16,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":16,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/towards-reliable-rare-category-analysis-on#ran","syntology_url":"https://syntology.ai/paper/2307.09858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09858"}},"official":{"repos":["wulongfeng/calirare"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/optiforest-optimal-isolation-forest-for","slug":"optiforest-optimal-isolation-forest-for","title":"OptIForest: Optimal Isolation Forest for Anomaly Detection","date":"2023-06-22","arxiv_id":"2306.12703","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/optiforest-optimal-isolation-forest-for#ran","syntology_url":"https://syntology.ai/paper/2306.12703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12703"}},"official":{"repos":["xiagll/optiforest"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sok-pragmatic-assessment-of-machine-learning","slug":"sok-pragmatic-assessment-of-machine-learning","title":"SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection","date":"2023-04-30","arxiv_id":"2305.00550","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sok-pragmatic-assessment-of-machine-learning#ran","syntology_url":"https://syntology.ai/paper/2305.00550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00550"}},"official":{"repos":["hihey54/pragmaticassessment"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/anoshift-a-distribution-shift-benchmark-for","slug":"anoshift-a-distribution-shift-benchmark-for","title":"AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection","date":"2022-06-30","arxiv_id":"2206.15476","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/anoshift-a-distribution-shift-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2206.15476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.15476"}},"official":{"repos":["bit-ml/anoshift"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/robustness-evaluation-of-deep-unsupervised","slug":"robustness-evaluation-of-deep-unsupervised","title":"Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems","date":"2022-06-25","arxiv_id":"2207.03576","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robustness-evaluation-of-deep-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2207.03576","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.03576"}},"official":{"repos":["intrudetection/robevalanodetect"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tod-tensor-based-outlier-detection","slug":"tod-tensor-based-outlier-detection","title":"TOD: GPU-accelerated Outlier Detection via Tensor Operations","date":"2021-10-26","arxiv_id":"2110.14007","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tod-tensor-based-outlier-detection#ran","syntology_url":"https://syntology.ai/paper/2110.14007","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14007"}},"official":{"repos":["yzhao062/pytod"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/e-graphsage-a-graph-neural-network-based","slug":"e-graphsage-a-graph-neural-network-based","title":"E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT","date":"2021-03-30","arxiv_id":"2103.16329","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/e-graphsage-a-graph-neural-network-based#ran","syntology_url":"https://syntology.ai/paper/2103.16329","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16329"}},"official":{"repos":["waimorris/E-GraphSAGE"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/anomalydae-dual-autoencoder-for-anomaly","slug":"anomalydae-dual-autoencoder-for-anomaly","title":"AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks","date":"2020-02-10","arxiv_id":"2002.03665","repositories_listed":3,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":6,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/anomalydae-dual-autoencoder-for-anomaly#ran","syntology_url":"https://syntology.ai/paper/2002.03665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.03665"}},"official":{"repos":["haoyfan/AnomalyDAE"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/kitsune-an-ensemble-of-autoencoders-for","slug":"kitsune-an-ensemble-of-autoencoders-for","title":"Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection","date":"2018-02-25","arxiv_id":"1802.09089","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/kitsune-an-ensemble-of-autoencoders-for#ran","syntology_url":"https://syntology.ai/paper/1802.09089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.09089"}},"official":{"repos":["ymirsky/KitNET-py"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-framework-for-validating-models-of-evasion","slug":"a-framework-for-validating-models-of-evasion","title":"Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features","date":"2017-08-28","arxiv_id":"1708.08327","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-framework-for-validating-models-of-evasion#ran","syntology_url":"https://syntology.ai/paper/1708.08327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.08327"}},"official":{"repos":["shinington/Robust-PDF-Classifier-with-Conserved-Features"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"9ea49343355deeb064e0a301bec209e8f72e696e70feff2520bf978512c8dd94","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}