LEADER 03894nam 22006255 450 001 9910366579403321 005 20200702232047.0 010 $a3-030-33664-6 024 7 $a10.1007/978-3-030-33664-6 035 $a(CKB)5280000000190203 035 $a(MiAaPQ)EBC6000783 035 $a(DE-He213)978-3-030-33664-6 035 $a(PPN)242820603 035 $a(EXLCZ)995280000000190203 100 $a20191219d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnomaly-Detection and Health-Analysis Techniques for Core Router Systems /$fby Shi Jin, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (155 pages) $cillustrations 311 $a3-030-33663-8 327 $aIntroduction -- Anomaly Detection Using Correlation-Based Time-Series Analysis -- Changepoint-based Anomaly Detection -- Hierarchical Symbol-based Health-Status Analysis -- Self-Learning Health-Status Analysis -- Conclusion. 330 $aThis book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today?s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; Includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status. 606 $aElectronic circuits 606 $aComputer engineering 606 $aInternet of things 606 $aEmbedded computer systems 606 $aElectrical engineering 606 $aCircuits and Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/T24068 606 $aCyber-physical systems, IoT$3https://scigraph.springernature.com/ontologies/product-market-codes/T24080 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aElectronic circuits. 615 0$aComputer engineering. 615 0$aInternet of things. 615 0$aEmbedded computer systems. 615 0$aElectrical engineering. 615 14$aCircuits and Systems. 615 24$aCyber-physical systems, IoT. 615 24$aCommunications Engineering, Networks. 676 $a004.6 700 $aJin$b Shi$4aut$4http://id.loc.gov/vocabulary/relators/aut$01064580 702 $aZhang$b Zhaobo$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aChakrabarty$b Krishnendu$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGu$b Xinli$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366579403321 996 $aAnomaly-Detection and Health-Analysis Techniques for Core Router Systems$92539289 997 $aUNINA