FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2955248800> ?p ?o ?g. }

• W2955248800 endingPage "88516" @default.
• W2955248800 startingPage "88504" @default.
• W2955248800 abstract "An IT system generates messages for other systems or users to consume, through direct interaction or as system logs. Automatically identifying the types of these machine-generated messages has many applications, such as intrusion detection and system behavior discovery. Among various heuristic methods for automatically identifying message types, the clustering methods based on keyword extraction have been quite effective. However, these methods still suffer from keyword misidentification problems, i.e., some keyword occurrences are wrongly identified as payload and some strings in the payload are wrongly identified as keyword occurrences, leading to the misidentification of the message types. In this paper, we propose a new machine language processing (MLP) approach, called <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${P}$ </tex-math></inline-formula> -gram, specifically designed for identifying keywords in, and subsequently clustering, machine-generated messages. First, we introduce a novel concept and technique, positional <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${n}$ </tex-math></inline-formula> -gram, for message keywords extraction. By associating the position as meta-data with each <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${n}$ </tex-math></inline-formula> -gram, we can more accurately discern which <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${n}$ </tex-math></inline-formula> -grams are keywords of a message and which <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${n}$ </tex-math></inline-formula> -grams are parts of the payload information. Then, the positional keywords are used as features to cluster the messages, and an entropy-based positional weighting method is devised to measure the importance or weight of the positional keywords to each message. Finally, a general centroid clustering method, <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${K}$ </tex-math></inline-formula> -Medoids, is used to leverage the importance of the keywords and cluster messages into groups reflecting their types. We evaluate our method on a range of machine-generated (text and binary) messages from the real-world systems and show that our method achieves higher accuracy than the current state-of-the-art tools." @default.
• W2955248800 created "2019-07-12" @default.
• W2955248800 creator A5020474429 @default.
• W2955248800 creator A5031271838 @default.
• W2955248800 creator A5050928648 @default.
• W2955248800 creator A5065022676 @default.
• W2955248800 creator A5072967605 @default.
• W2955248800 creator A5076014464 @default.
• W2955248800 creator A5089525777 @default.
• W2955248800 date "2019-01-01" @default.
• W2955248800 modified "2023-10-16" @default.
• W2955248800 title "P-Gram: Positional N-Gram for the Clustering of Machine-Generated Messages" @default.
• W2955248800 cites W1972428331 @default.
• W2955248800 cites W2046640879 @default.
• W2955248800 cites W2049867480 @default.
• W2955248800 cites W2053519393 @default.
• W2955248800 cites W2055228862 @default.
• W2955248800 cites W2056325176 @default.
• W2955248800 cites W2080329164 @default.
• W2955248800 cites W2086320398 @default.
• W2955248800 cites W2236523039 @default.
• W2955248800 cites W2336069085 @default.
• W2955248800 cites W239045340 @default.
• W2955248800 cites W2527994611 @default.
• W2955248800 cites W2598269246 @default.
• W2955248800 cites W2612984812 @default.
• W2955248800 cites W2760820633 @default.
• W2955248800 cites W2770711471 @default.
• W2955248800 cites W2889492109 @default.
• W2955248800 cites W2899449303 @default.
• W2955248800 cites W38765094 @default.
• W2955248800 cites W4213009331 @default.
• W2955248800 doi "https://doi.org/10.1109/access.2019.2924928" @default.
• W2955248800 hasPublicationYear "2019" @default.
• W2955248800 type Work @default.
• W2955248800 sameAs 2955248800 @default.
• W2955248800 citedByCount "6" @default.
• W2955248800 countsByYear W29552488002020 @default.
• W2955248800 countsByYear W29552488002021 @default.
• W2955248800 countsByYear W29552488002023 @default.
• W2955248800 crossrefType "journal-article" @default.
• W2955248800 hasAuthorship W2955248800A5020474429 @default.
• W2955248800 hasAuthorship W2955248800A5031271838 @default.
• W2955248800 hasAuthorship W2955248800A5050928648 @default.
• W2955248800 hasAuthorship W2955248800A5065022676 @default.
• W2955248800 hasAuthorship W2955248800A5072967605 @default.
• W2955248800 hasAuthorship W2955248800A5076014464 @default.
• W2955248800 hasAuthorship W2955248800A5089525777 @default.
• W2955248800 hasBestOaLocation W29552488001 @default.
• W2955248800 hasConcept C117884012 @default.
• W2955248800 hasConcept C134066672 @default.
• W2955248800 hasConcept C137293760 @default.
• W2955248800 hasConcept C154945302 @default.
• W2955248800 hasConcept C158379750 @default.
• W2955248800 hasConcept C161369605 @default.
• W2955248800 hasConcept C23123220 @default.
• W2955248800 hasConcept C31258907 @default.
• W2955248800 hasConcept C33923547 @default.
• W2955248800 hasConcept C41008148 @default.
• W2955248800 hasConcept C45357846 @default.
• W2955248800 hasConcept C523546767 @default.
• W2955248800 hasConcept C54355233 @default.
• W2955248800 hasConcept C73555534 @default.
• W2955248800 hasConcept C86803240 @default.
• W2955248800 hasConcept C94375191 @default.
• W2955248800 hasConceptScore W2955248800C117884012 @default.
• W2955248800 hasConceptScore W2955248800C134066672 @default.
• W2955248800 hasConceptScore W2955248800C137293760 @default.
• W2955248800 hasConceptScore W2955248800C154945302 @default.
• W2955248800 hasConceptScore W2955248800C158379750 @default.
• W2955248800 hasConceptScore W2955248800C161369605 @default.
• W2955248800 hasConceptScore W2955248800C23123220 @default.
• W2955248800 hasConceptScore W2955248800C31258907 @default.
• W2955248800 hasConceptScore W2955248800C33923547 @default.
• W2955248800 hasConceptScore W2955248800C41008148 @default.
• W2955248800 hasConceptScore W2955248800C45357846 @default.
• W2955248800 hasConceptScore W2955248800C523546767 @default.
• W2955248800 hasConceptScore W2955248800C54355233 @default.
• W2955248800 hasConceptScore W2955248800C73555534 @default.
• W2955248800 hasConceptScore W2955248800C86803240 @default.
• W2955248800 hasConceptScore W2955248800C94375191 @default.
• W2955248800 hasFunder F4320334704 @default.
• W2955248800 hasLocation W29552488001 @default.
• W2955248800 hasLocation W29552488002 @default.
• W2955248800 hasLocation W29552488003 @default.
• W2955248800 hasOpenAccess W2955248800 @default.
• W2955248800 hasPrimaryLocation W29552488001 @default.
• W2955248800 hasRelatedWork W1575639387 @default.
• W2955248800 hasRelatedWork W2088254117 @default.
• W2955248800 hasRelatedWork W2357241418 @default.
• W2955248800 hasRelatedWork W2747014888 @default.
• W2955248800 hasRelatedWork W2790860321 @default.
• W2955248800 hasRelatedWork W2906970013 @default.
• W2955248800 hasRelatedWork W2959686711 @default.
• W2955248800 hasRelatedWork W3033292598 @default.
• W2955248800 hasRelatedWork W3126081632 @default.
• W2955248800 hasRelatedWork W4254593385 @default.
• W2955248800 hasVolume "7" @default.

• next