FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1421736293> ?p ?o ?g. }

• W1421736293 abstract "Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made onmore » this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.« less" @default.
• W1421736293 created "2016-06-24" @default.
• W1421736293 creator A5077865858 @default.
• W1421736293 date "2013-12-01" @default.
• W1421736293 modified "2023-09-25" @default.
• W1421736293 title "Anomaly Detection in Dynamic Networks" @default.
• W1421736293 cites W130710483 @default.
• W1421736293 cites W135124929 @default.
• W1421736293 cites W1483307070 @default.
• W1421736293 cites W1577117059 @default.
• W1421736293 cites W1594031697 @default.
• W1421736293 cites W1652518046 @default.
• W1421736293 cites W1661915592 @default.
• W1421736293 cites W1684305122 @default.
• W1421736293 cites W1964274671 @default.
• W1421736293 cites W1967042859 @default.
• W1421736293 cites W1978129804 @default.
• W1421736293 cites W1991485860 @default.
• W1421736293 cites W2007063660 @default.
• W1421736293 cites W2007087405 @default.
• W1421736293 cites W2014426991 @default.
• W1421736293 cites W2017899835 @default.
• W1421736293 cites W2022758229 @default.
• W1421736293 cites W2027255954 @default.
• W1421736293 cites W2029976685 @default.
• W1421736293 cites W2032280284 @default.
• W1421736293 cites W2033057584 @default.
• W1421736293 cites W2041414850 @default.
• W1421736293 cites W2048422603 @default.
• W1421736293 cites W2053615395 @default.
• W1421736293 cites W2053831332 @default.
• W1421736293 cites W2057797785 @default.
• W1421736293 cites W2064119066 @default.
• W1421736293 cites W2076092328 @default.
• W1421736293 cites W2091442045 @default.
• W1421736293 cites W2094990982 @default.
• W1421736293 cites W2106706098 @default.
• W1421736293 cites W2111787305 @default.
• W1421736293 cites W2113166878 @default.
• W1421736293 cites W2113377603 @default.
• W1421736293 cites W2117095967 @default.
• W1421736293 cites W2117270233 @default.
• W1421736293 cites W2119338125 @default.
• W1421736293 cites W2122646361 @default.
• W1421736293 cites W2122923407 @default.
• W1421736293 cites W2129117219 @default.
• W1421736293 cites W2132914434 @default.
• W1421736293 cites W2140273660 @default.
• W1421736293 cites W2146950091 @default.
• W1421736293 cites W2147357149 @default.
• W1421736293 cites W2158940042 @default.
• W1421736293 cites W2163605054 @default.
• W1421736293 cites W2168634963 @default.
• W1421736293 cites W2169670178 @default.
• W1421736293 cites W2171166366 @default.
• W1421736293 cites W2171304305 @default.
• W1421736293 cites W2171331105 @default.
• W1421736293 cites W2215703517 @default.
• W1421736293 cites W2502872609 @default.
• W1421736293 cites W2951199045 @default.
• W1421736293 cites W3004389950 @default.
• W1421736293 cites W42722137 @default.
• W1421736293 doi "https://doi.org/10.25560/24673" @default.
• W1421736293 hasPublicationYear "2013" @default.
• W1421736293 type Work @default.
• W1421736293 sameAs 1421736293 @default.
• W1421736293 citedByCount "1" @default.
• W1421736293 countsByYear W14217362932017 @default.
• W1421736293 crossrefType "dissertation" @default.
• W1421736293 hasAuthorship W1421736293A5077865858 @default.
• W1421736293 hasConcept C100906024 @default.
• W1421736293 hasConcept C105795698 @default.
• W1421736293 hasConcept C107673813 @default.
• W1421736293 hasConcept C111350023 @default.
• W1421736293 hasConcept C11413529 @default.
• W1421736293 hasConcept C121332964 @default.
• W1421736293 hasConcept C124101348 @default.
• W1421736293 hasConcept C126042441 @default.
• W1421736293 hasConcept C127413603 @default.
• W1421736293 hasConcept C12997251 @default.
• W1421736293 hasConcept C134306372 @default.
• W1421736293 hasConcept C154945302 @default.
• W1421736293 hasConcept C158424031 @default.
• W1421736293 hasConcept C164660894 @default.
• W1421736293 hasConcept C26873012 @default.
• W1421736293 hasConcept C2776214188 @default.
• W1421736293 hasConcept C33923547 @default.
• W1421736293 hasConcept C41008148 @default.
• W1421736293 hasConcept C62611344 @default.
• W1421736293 hasConcept C66938386 @default.
• W1421736293 hasConcept C739882 @default.
• W1421736293 hasConcept C76155785 @default.
• W1421736293 hasConceptScore W1421736293C100906024 @default.
• W1421736293 hasConceptScore W1421736293C105795698 @default.
• W1421736293 hasConceptScore W1421736293C107673813 @default.
• W1421736293 hasConceptScore W1421736293C111350023 @default.
• W1421736293 hasConceptScore W1421736293C11413529 @default.
• W1421736293 hasConceptScore W1421736293C121332964 @default.
• W1421736293 hasConceptScore W1421736293C124101348 @default.
• W1421736293 hasConceptScore W1421736293C126042441 @default.

• next