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020 _a9781846282539
_99781846282539
024 7 _a10.1007/1846282535
_2doi
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039 9 _a201509030750
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050 4 _aQA75.5-76.95
100 1 _aMaloof, Marcus A.
_eeditor.
_9322679
245 1 0 _aMachine Learning and Data Mining for Computer Security :
_bMethods and Applications /
_cedited by Marcus A. Maloof.
264 1 _aLondon :
_bSpringer London,
_c2006.
300 _axvI, 210 páginas 23 ilustraciones
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aAdvanced Information and Knowledge Processing
500 _aSpringer eBooks
505 0 _aSurvey Contributions -- An Introduction to Information Assurance -- Some Basic Concept of Machine Learning and Data Mining -- Research Contributions -- Learning to Detect Malicious Executables -- Data Mining Applied to Intrusion Detection: MITRE Experiences -- Intrusion Detection Alarm Clustering -- Behavioral Features for Network Anomaly Detection -- Cost-Sensitive Modeling for Intrusion Detection -- Data Cleaning and Enriched Representations for Anomaly Detection in System Calls -- A Decision-Theoritic, Semi-Supervised Model for Intrusion Detection.
520 _aThe Internet began as a private network connecting government, military, and academic researchers. As such, there was little need for secure protocols, encrypted packets, and hardened servers. When the creation of the World Wide Web unexpectedly ushered in the age of the commercial Internet, the network's size and subsequent rapid expansion made it impossible retroactively to apply secure mechanisms. The Internet's architects never coined terms such as spam, phishing, zombies, and spyware, but they are terms and phenomena we now encounter constantly. Programming detectors for such threats has proven difficult. Put simply, there is too much information---too many protocols, too many layers, too many applications, and too many uses of these applications---for anyone to make sufficient sense of it all. Ironically, given this wealth of information, there is also too little information about what is important for detecting attacks. Methods of machine learning and data mining can help build better detectors from massive amounts of complex data. Such methods can also help discover the information required to build more secure systems. For some problems in computer security, one can directly apply machine learning and data mining techniques. Other problems, both current and future, require new approaches, methods, and algorithms. This book presents research conducted in academia and industry on methods and applications of machine learning and data mining for problems in computer security and will be of interest to researchers and practitioners, as well students. ‘Dr. Maloof not only did a masterful job of focusing the book on a critical area that was in dire need of research, but he also strategically picked papers that complemented each other in a productive manner. … This book is a must read for anyone interested in how research can improve computer security.’ Dr Eric Cole, Computer Security Expert
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781846280290
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-253-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c291407
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