02593oam 2200505 450 991078756650332120190911103513.00-429-16687-71-4665-8209-X10.1201/b15088 (OCoLC)856570433(MiFhGG)GVRL8QDF(EXLCZ)99267000000040063320130425h20142014 uy 0engurun|---uuuuatxtccrNetwork anomaly detection a machine learning perspective /Dhruba Kumar Bhattacharyya, Jugal Kumar KalitaBoca Raton :CRC Press, Taylor & Francis Group,[2014]�20141 online resource (xxv, 340 pages) illustrationsGale eBooksDescription based upon print version of record.1-4665-8208-1 Includes bibliographical references.Front 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; ReferencesThis 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--Provided by publisher.Computer networksSecurity measuresIntrusion detection systems (Computer security)Machine learningComputer networksSecurity measures.Intrusion detection systems (Computer security)Machine learning.005.8COM037000COM053000COM083000bisacshBhattacharyya Dhruba K.1571999Kalita Jugal KumarMiFhGGMiFhGGBOOK9910787566503321Network anomaly detection3846587UNINA