Data Warehousing and Data Mining Techniques for Cyber Security

• Κύριος συγγραφέας: Singhal, Anoop (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2007.
• Σειρά: Advances in Information Security, 31
• Θέματα: Computer science.; Computer communication systems.; Computer security.; Data structures (Computer science).; Data encryption (Computer science).; Database management.; Data mining.; Computer Science.; Systems and Data Security.; Computer Communication Networks.; Database Management.; Data Mining and Knowledge Discovery.; Data Structures, Cryptology and Information Theory.; Data Encryption.

 Data warehousing and data mining provide techniques for collecting information from distributed databases and for performing data analysis. The ever expanding, tremendous amount of data collected and stored in large databases has far exceeded our human ability to comprehend--without the proper tools. There is a critical need for data analysis that can automatically analyze data, summarize it and predict future trends. In the modern age of Internet connectivity, concerns about denial of service attacks, computer viruses and worms are extremely important. Data Warehousing and Data Mining Techniques for Cyber Security contributes to the discipline of security informatics. The author discusses topics that intersect cyber security and data mining, while providing techniques for improving cyber security. Since the cost of information processing and internet accessibility is dropping, an increasing number of organizations are becoming vulnerable to cyber attacks. This volume introduces techniques for applications in the area of retail, finance, and bioinformatics, to name a few. Data Warehousing and Data Mining Techniques for Cyber Security is designed for practitioners and researchers in industry. This book is also suitable for upper-undergraduate and graduate-level students in computer science. 

 An Overview of Data Warehouse, OLAP and Data Mining Technology -- Network and System Security -- Intrusion Detection Systems -- Data Mining for Intrusion Detection -- Data Modeling and Data Warehousing Techniques to Improve Intrusion Detection -- Minds: Architecture & Design -- Discovering Novel Attack Strategies from Infosec Alerts.