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{ "labelLang" : "eng", "responseDate" : "2026-02-13 04:34", "paging" : { "last" : false, "first" : true, "totalPages" : 11, "totalElements" : 103, "totalUncutElements" : 103, "totalEstimatedElements" : 20, "size" : 10, "number" : 1, "numberOfElements" : 10, "sort" : [ { "direction" : "DESC", "property" : "publishedYear", "ascending" : false }, { "direction" : "ASC", "property" : "firstAuthor", "ascending" : true } ] }, "content" : [ { "otype" : "JournalArticle", "mtid" : 36880483, "status" : "ADMIN_APPROVED", "type" : { "otype" : "PublicationType", "mtid" : 24, "link" : "/api/publicationtype/24", "label" : "Journal Article", "code" : 24, "otypeName" : "JournalArticle", "listPosition" : 1, "published" : true, "oldId" : 24, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 10000059, "mayHaveImpactFactor" : true, "link" : "/api/subtype/10000059", "label" : "Article (Journal Article)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2026, "abstractText" : "In recent decades, significant research effort has been put into developing solutions to support automatized or semi-automatized analysis of log files. A large number of algorithms appeared based on neural networks. In this paper we introduce a new approach to anomaly detection in log files, that does not rely on neural networks. The building blocks of our approach have been well-known in machine learning for a long time. We propose to use a weighted Damerau-Levenshtein distance metric to quantify the similarity between log sequences. We introduce a kNN-based algorithm for semi-supervised log anomaly detection, and an HDBSCAN-based solution for the unsupervised problem. For the latter, we extend the algorithm by incorporating a manual feedback mechanism, enabling human domain experts to modify sequence labels when necessary.", "citationCount" : 0, "citationCountWoOther" : 0, "independentCitCountWoOther" : 0, "independentCitationCount" : 0, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 0, "independentCitingPubCount" : 0, "citingPubCountWoOther" : 0, "independentCitingPubCountWoOther" : 0, "unhandledCitingPubCount" : 0, "link" : "/api/publication/36880483", "label" : "Horváth Gábor et al. Detecting anomalies in log files using the Damerau-Levenshtein distance metric. (2026) DATA MINING AND KNOWLEDGE DISCOVERY 1384-5810 1573-756X 40 1", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "BookChapter", "mtid" : 36925438, "status" : "APPROVED", "type" : { "otype" : "PublicationType", "mtid" : 25, "link" : "/api/publicationtype/25", "label" : "Chapter in Book", "code" : 25, "otypeName" : "BookChapter", "listPosition" : 2, "published" : true, "oldId" : 25, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 10000312, "mayHaveImpactFactor" : false, "link" : "/api/subtype/10000312", "label" : "Conference paper (Chapter in Book)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2025, "citationCount" : 0, "citationCountWoOther" : 0, "independentCitCountWoOther" : 0, "independentCitationCount" : 0, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 0, "independentCitingPubCount" : 0, "citingPubCountWoOther" : 0, "independentCitingPubCountWoOther" : 0, "unhandledCitingPubCount" : 0, "link" : "/api/publication/36925438", "label" : "Szalai M. Dániel et al. LODA Revisited: Enhancements for Robust Online Anomaly Detection with Concept Drift Handling. (2025) In: 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 933-938", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "JournalArticle", "mtid" : 32242266, "status" : "VALIDATED", "type" : { "otype" : "PublicationType", "mtid" : 24, "link" : "/api/publicationtype/24", "label" : "Journal Article", "code" : 24, "otypeName" : "JournalArticle", "listPosition" : 1, "published" : true, "oldId" : 24, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 10000249, "mayHaveImpactFactor" : false, "link" : "/api/subtype/10000249", "label" : "Comment, Correction (Journal Article)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2024, "abstractText" : "Affiliation for author Miklos Telek was missing in the original publication and should be read as: Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary and MTA-BME Information Systems Reseach Group, Hungary. Along with Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary. Original article has been updated. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.", "citationCount" : 0, "citationCountWoOther" : 0, "independentCitCountWoOther" : 0, "independentCitationCount" : 0, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 0, "independentCitingPubCount" : 0, "citingPubCountWoOther" : 0, "independentCitingPubCountWoOther" : 0, "unhandledCitingPubCount" : 0, "link" : "/api/publication/32242266", "label" : "Almousa S.A.-D. et al. Correction to: Transient analysis of piecewise homogeneous Markov fluid models (Annals of Operations Research, (2020), 10.1007/s10479-020-03831-1). (2024) ANNALS OF OPERATIONS RESEARCH 0254-5330 1572-9338 332 1251-1251", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "Patent", "mtid" : 35076182, "status" : "APPROVED", "type" : { "otype" : "PublicationType", "mtid" : 26, "link" : "/api/publicationtype/26", "label" : "Protection forms", "code" : 26, "otypeName" : "Patent", "listPosition" : 7, "published" : true, "oldId" : 26, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 21, "mayHaveImpactFactor" : false, "link" : "/api/subtype/21", "label" : "International patent (Protection forms)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2024, "citationCount" : 0, "citationCountWoOther" : 0, "independentCitCountWoOther" : 0, "independentCitationCount" : 0, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 0, "independentCitingPubCount" : 0, "citingPubCountWoOther" : 0, "independentCitingPubCountWoOther" : 0, "unhandledCitingPubCount" : 0, "link" : "/api/publication/35076182", "label" : "Péter SZILÁGYI et al. Anomaly Detection Using Logs. (2024) WO/2024/074883", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "JournalArticle", "mtid" : 33629258, "status" : "ADMIN_APPROVED", "type" : { "otype" : "PublicationType", "mtid" : 24, "link" : "/api/publicationtype/24", "label" : "Journal Article", "code" : 24, "otypeName" : "JournalArticle", "listPosition" : 1, "published" : true, "oldId" : 24, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 10000059, "mayHaveImpactFactor" : true, "link" : "/api/subtype/10000059", "label" : "Article (Journal Article)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2023, "abstractText" : "We propose a method called Bilateral-Weighted Online Adaptive Isolation Forest (BWOAIF) for unsupervised anomaly detection based on Isolation Forest (IF), which is applicable to streaming data and able to cope with concept drift. Similar to IF, the proposed method has only few hyperparameters whose effect on the performance are easy to interpret by human intuition and therefore easy to tune. BWOAIF ingests data and classifies it as normal or anomalous, and simultaneously adapts its classifier by removing old trees as well as by creating new ones. We show that BWOAIF adapts gradually to slow concept drifts, and, at the same time, it is able to adapt fast to sudden changes of the data distribution. Numerical results show the efficacy of the proposed algorithm and its ability to learn different classes of concept drifts, such as slow/fast concept shift, concept split, concept appearance, and concept disappearance.", "citationCount" : 2, "citationCountWoOther" : 2, "independentCitCountWoOther" : 2, "independentCitationCount" : 2, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 2, "independentCitingPubCount" : 2, "citingPubCountWoOther" : 2, "independentCitingPubCountWoOther" : 2, "unhandledCitingPubCount" : 0, "link" : "/api/publication/33629258", "label" : "Hannák Gábor et al. Bilateral-Weighted Online Adaptive Isolation Forest for anomaly detection in streaming data. (2023) STATISTICAL ANALYSIS AND DATA MINING 1932-1864 1932-1872 16 3 215-223", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "JournalArticle", "mtid" : 33879816, "status" : "ADMIN_APPROVED", "type" : { "otype" : "PublicationType", "mtid" : 24, "link" : "/api/publicationtype/24", "label" : "Journal Article", "code" : 24, "otypeName" : "JournalArticle", "listPosition" : 1, "published" : true, "oldId" : 24, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 10000059, "mayHaveImpactFactor" : true, "link" : "/api/subtype/10000059", "label" : "Article (Journal Article)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2023, "citationCount" : 1, "citationCountWoOther" : 1, "independentCitCountWoOther" : 1, "independentCitationCount" : 1, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 1, "independentCitingPubCount" : 1, "citingPubCountWoOther" : 1, "independentCitingPubCountWoOther" : 1, "unhandledCitingPubCount" : 0, "link" : "/api/publication/33879816", "label" : "Horváth G et al. TeleDAL: a regression-based template-less unsupervised method for finding anomalies in log sequences. (2023) JOURNAL OF SUPERCOMPUTING 0920-8542 1573-0484 79 16 18394-18416", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "JournalArticle", "mtid" : 33794293, "status" : "APPROVED", "type" : { "otype" : "PublicationType", "mtid" : 24, "link" : "/api/publicationtype/24", "label" : "Journal Article", "code" : 24, "otypeName" : "JournalArticle", "listPosition" : 1, "published" : true, "oldId" : 24, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 10000059, "mayHaveImpactFactor" : true, "link" : "/api/subtype/10000059", "label" : "Article (Journal Article)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2023, "citationCount" : 1, "citationCountWoOther" : 1, "independentCitCountWoOther" : 1, "independentCitationCount" : 1, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 1, "independentCitingPubCount" : 1, "citingPubCountWoOther" : 1, "independentCitingPubCountWoOther" : 1, "unhandledCitingPubCount" : 0, "link" : "/api/publication/33794293", "label" : "Horváth Gábor et al. The Sub-Sequence Summary Method for Detecting Anomalies in Logs. (2023) IEEE ACCESS 2169-3536 11 37412-37423", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "Patent", "mtid" : 35076205, "status" : "APPROVED", "type" : { "otype" : "PublicationType", "mtid" : 26, "link" : "/api/publicationtype/26", "label" : "Protection forms", "code" : 26, "otypeName" : "Patent", "listPosition" : 7, "published" : true, "oldId" : 26, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 23, "mayHaveImpactFactor" : false, "link" : "/api/subtype/23", "label" : "USA patent (Protection forms)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2023, "citationCount" : 0, "citationCountWoOther" : 0, "independentCitCountWoOther" : 0, "independentCitationCount" : 0, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 0, "independentCitingPubCount" : 0, "citingPubCountWoOther" : 0, "independentCitingPubCountWoOther" : 0, "unhandledCitingPubCount" : 0, "link" : "/api/publication/35076205", "label" : "Péter SZILÁGYI et al. Method and Apparatus for Anomaly Detection. (2023) US20230412627", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "JournalArticle", "mtid" : 32897098, "status" : "VALIDATED", "type" : { "otype" : "PublicationType", "mtid" : 24, "link" : "/api/publicationtype/24", "label" : "Journal Article", "code" : 24, "otypeName" : "JournalArticle", "listPosition" : 1, "published" : true, "oldId" : 24, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 1268360, "mayHaveImpactFactor" : false, "link" : "/api/subtype/1268360", "label" : "Conference paper in journal (Journal Article)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2022, "abstractText" : "Numerical inverse Laplace transformation (NILT) is an important tool in the field of system modelling and performance analysis. The recently introduced CME method has many important advantages over the alternative numerical inverse Laplace transformation (NILT) methods. It avoids Gibbs oscillation (i.e., does not generate overshoot and undershoot), preserves the monotonicity of functions, its accuracy is gradually improving with the order, and it is numerically more stable than the alternative methods. In this paper we demonstrate these advantages and introduce our tool which implements the CME method and other popular NILT methods.", "citationCount" : 0, "citationCountWoOther" : 0, "independentCitCountWoOther" : 0, "independentCitationCount" : 0, "selfCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 0, "independentCitingPubCount" : 0, "citingPubCountWoOther" : 0, "independentCitingPubCountWoOther" : 0, "unhandledCitingPubCount" : 0, "link" : "/api/publication/32897098", "label" : "Almousa S.A.-D. et al. The CME method: Efficient numerical inverse Laplace transformation with Concentrated Matrix Exponential distribution. (2022) SIGMETRICS PERFORMANCE EVALUATION REVIEW 0163-5999 1557-9484 49 4 29-34", "snippet" : true, "template" : "", "template2" : "" }, { "otype" : "JournalArticle", "mtid" : 31797535, "status" : "VALIDATED", "type" : { "otype" : "PublicationType", "mtid" : 24, "link" : "/api/publicationtype/24", "label" : "Journal Article", "code" : 24, "otypeName" : "JournalArticle", "listPosition" : 1, "published" : true, "oldId" : 24, "snippet" : true }, "subType" : { "otype" : "SubType", "mtid" : 10000059, "mayHaveImpactFactor" : true, "link" : "/api/subtype/10000059", "label" : "Article (Journal Article)", "snippet" : true }, "category" : { "otype" : "Category", "mtid" : 1, "link" : "/api/category/1", "label" : "Scientific", "published" : true, "oldId" : 1, "snippet" : true }, "languages" : [ { "otype" : "Language", "mtid" : 10002, "link" : "/api/language/10002", "label" : "English", "name" : "Angol", "nameEng" : "English", "published" : true, "oldId" : 2, "snippet" : true } ], "publishedYear" : 2022, "abstractText" : "Piecewise homogeneous Markov fluid models are composed by homogeneous intervals where the model is governed by an interval dependent pair of generators and the model behaviour changes at the boundaries. The main difficulty of the transient analysis of piecewise homogeneous Markov fluid models is the appropriate description of the various boundary cases. The paper proposes an analytical approach to handle the wide variety of the possible boundary cases in a relatively simple to describe and implement manner.", "citationCount" : 6, "citationCountWoOther" : 6, "independentCitCountWoOther" : 2, "independentCitationCount" : 2, "selfCitationCount" : 4, "unhandledCitationCount" : 0, "citingPubCount" : 6, "independentCitingPubCount" : 2, "citingPubCountWoOther" : 6, "independentCitingPubCountWoOther" : 2, "unhandledCitingPubCount" : 0, "link" : "/api/publication/31797535", "label" : "Almousa Salah Al-Deen et al. Transient analysis of piecewise homogeneous Markov fluid models. (2022) ANNALS OF OPERATIONS RESEARCH 0254-5330 1572-9338 310 2 333-353", "snippet" : true, "template" : "", "template2" : "" } ] }

Last modified: 2014.09.15.

© Gábor Horváth, 2012