Download Data Mining and Machine Learning in Cybersecurity by Sumeet Dua, Xian Du PDF

By Sumeet Dua, Xian Du

With the speedy development of knowledge discovery strategies, computing device studying and information mining proceed to play an important position in cybersecurity. even though numerous meetings, workshops, and journals concentrate on the fragmented examine issues during this region, there was no unmarried interdisciplinary source on prior and present works and attainable paths for destiny study during this sector. This ebook fills this need.

From easy ideas in laptop studying and information mining to complex difficulties within the computer studying area, Data Mining and desktop studying in Cybersecurity offers a unified reference for particular desktop studying options to cybersecurity difficulties. It offers a origin in cybersecurity basics and surveys modern challenges—detailing state-of-the-art desktop studying and knowledge mining thoughts. It additionally:

• Unveils state of the art suggestions for detecting new attacks
• comprises in-depth discussions of laptop studying suggestions to detection problems
• Categorizes tools for detecting, scanning, and profiling intrusions and anomalies
• Surveys modern cybersecurity difficulties and unveils cutting-edge laptop studying and information mining recommendations
• info privacy-preserving facts mining tools

This interdisciplinary source comprises process evaluate tables that permit for fast entry to universal cybersecurity difficulties and linked facts mining equipment. quite a few illustrative figures aid readers visualize the workflow of advanced options and greater than 40 case reports supply a transparent realizing of the layout and alertness of knowledge mining and laptop studying concepts in cybersecurity.

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Vaidya. Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. In: Proceedings of the 2006 ACM Symposium on Applied Computing, Dijon, France, 2006. Zhang, J. and M. Zulkernine. A hybrid network intrusion detection technique using random forests. In: Proceedings of the First International Conference on Availability, Reliability and Security, 2006a, pp. 262–269. Zhang, J. and M. Zulkernine. Anomaly based network intrusion detection with unsupervised outlier detection. In: IEEE International Conference on Communications, Istanbul, Turkey, 2006b.

Using decision trees to improve signature-based intrusion detection. In: Proceedings of the 6th International Workshop on the Recent Advances in Intrusion Detection, West Lafayette, IN, 2003, pp. 173–191. , M. Crovella, and C. Diot. Characterization of network-wide anomalies in traffic flows. In: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, Taormina, Sicily, Italy, 2004, pp. 201–206. , M. Crovella, and C. Diot. Mining anomalies using traffic feature distributions. In: Proceedings of the 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, Philadelphia, PA, 2005.

In: Proceedings of the 1998 National Information Systems Security Conference (NISSC ’98), Arlington, VA, 1998, pp. 443–456. , E. Banerjee et al. Data mining for cyber security. In: Data Warehousing and Data Mining Techniques for Computer Security, edited by A. Singhal. Springer, New York, 2006. , A. P. Thomas. Feature deduction and ensemble design of intrusion detection systems. Computers & Security 24 (2005): 1–13. H. P. Shen. Application of SVM and ANN for intrusion detection. Computers & Operations Research 32 (2005): 2617–2634.

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