LEADER 01129nam--2200385---450- 001 990002370040203316 005 20101111090814.0 035 $a000237004 035 $aUSA01000237004 035 $a(ALEPH)000237004USA01 035 $a000237004 100 $a20050119d1979----km-y0itay0103----ba 101 0 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aGramsci a Vienna$ericerche e documenti, 1922-1924$fGiovanni Somai 210 $aUrbino$cArgalia$d1979 215 $a212 p.$d21 cm 225 2 $aStudi storici 410 0$12001$aStudi storici 454 1$12001 461 1$1001-------$12001 700 1$aSOMAI,$bGiovanni$0192324 801 0$aIT$bsalbc$gISBD 912 $a990002370040203316 951 $a320.1 SOM 1 (Coll. IS 3)$b52824 G.$cColl. IS$d00253383 959 $aBK 969 $aUMA 979 $aSIAV7$b10$c20050119$lUSA01$h1604 979 $aRSIAV3$b90$c20091216$lUSA01$h1553 979 $aRSIAV1$b90$c20100329$lUSA01$h1235 979 $aRSIAV2$b90$c20101111$lUSA01$h0907 979 $aRSIAV2$b90$c20101111$lUSA01$h0908 996 $aGramsci a Vienna$9518865 997 $aUNISA LEADER 02593oam 2200505 450 001 9910818815903321 005 20190911103513.0 010 $a0-429-16687-7 010 $a1-4665-8209-X 024 7 $a10.1201/b15088 035 $a(OCoLC)856570433 035 $a(MiFhGG)GVRL8QDF 035 $a(EXLCZ)992670000000400633 100 $a20130425h20142014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aNetwork anomaly detection $ea machine learning perspective /$fDhruba Kumar Bhattacharyya, Jugal Kumar Kalita 210 1$aBoca Raton :$cCRC Press, Taylor & Francis Group,$d[2014] 210 4$d?2014 215 $a1 online resource (xxv, 340 pages) $cillustrations 225 0 $aGale eBooks 300 $aDescription based upon print version of record. 311 $a1-4665-8208-1 320 $aIncludes bibliographical references. 327 $aFront Cover; Dedication; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Abstract; Authors; 1. Introduction; 2. Networks and Anomalies; 3. An Overview of Machine Learning Methods; 4. Detecting Anomalies in Network Data; 5. Feature Selection; 6. Approaches to Network Anomaly Detection; 7. Evaluation Methods; 8. Tools and Systems; 9. Open Issues, Challenges and Concluding Remarks; References 330 $aThis book discusses detection of anomalies in computer networks from a machine learning perspective. It introduces readers to how computer networks work and how they can be attacked by intruders in search of fame, fortune, or challenge. The reader will learn how one can look for patterns in captured network traffic data to look for anomalous patterns that may correspond to attempts at unauthorized intrusion. The reader will be given a technical and sophisticated description of such algorithms and their applications in the context of intrusion detection in networks--$cProvided by publisher. 606 $aComputer networks$xSecurity measures 606 $aIntrusion detection systems (Computer security) 606 $aMachine learning 615 0$aComputer networks$xSecurity measures. 615 0$aIntrusion detection systems (Computer security) 615 0$aMachine learning. 676 $a005.8 686 $aCOM037000$aCOM053000$aCOM083000$2bisacsh 700 $aBhattacharyya$b Dhruba K.$01611379 702 $aKalita$b Jugal Kumar 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910818815903321 996 $aNetwork anomaly detection$93939628 997 $aUNINA