Data mining and machine learning in cybersecurity / Sumeet Dua, Xian Du.

1. Introduction -- 2. Classical machine-learning paradigms for data mining -- 3. Supervised learning for misuse/signature detection -- 4. Machine learning for anomaly detection -- 5. Machine learning for hybrid detection -- 6. Machine learning for scan detection -- 7. Machine learning for profiling network traffic -- 8. Privacy-preserving data mining -- 9. Emerging challenges in cybersecurity.

"Introducing basic concepts of machine learning and data mining methodologies for cyber security, this book provides a unified reference for specific machine learning solutions and cybersecurity problems. The authors focus on how to apply machine learning methodologies in cybersecurity, categorizing methods for detecting, scanning, profiling, intrusions, and anomalies. The text presents challenges and solutions in machine learning along with cybersecurity fundamentals. It also describes advanced problems in cybersecurity in the machine learning domain and examines privacy-preserving data mining methods as a proactive security solution"-- Provided by publisher.

"This interdisciplinary assessment is especially useful for students, who typically learn cybersecurity, machine learning, and data mining in independent courses. Machine learning and data mining play significant roles in cybersecurity, especially as more challenges appear with the rapid development of information discovery techniques, such as those originating from the sheer dimensionality and heterogeneous nature of the network data, the dynamic change of threats, and the severe imbalanced classes of normal and anomalous behaviors"-- Provided by publisher.

Includes bibliographical references and index.