By Sumeet Dua, Xian Du
With the quick development of knowledge discovery options, laptop studying and knowledge mining proceed to play an important position in cybersecurity. even if numerous meetings, workshops, and journals specialise in the fragmented learn subject matters during this quarter, there was no unmarried interdisciplinary source on earlier and present works and attainable paths for destiny examine during this region. This ebook fills this need.
From easy ideas in laptop studying and information mining to complicated difficulties within the computing device studying area, Data Mining and computer studying in Cybersecurity offers a unified reference for particular computer studying recommendations to cybersecurity difficulties. It offers a origin in cybersecurity basics and surveys modern challenges—detailing state-of-the-art computer studying and knowledge mining options. It additionally:
• Unveils state of the art thoughts for detecting new attacks
• comprises in-depth discussions of computing device studying ideas to detection problems
• Categorizes tools for detecting, scanning, and profiling intrusions and anomalies
• Surveys modern cybersecurity difficulties and unveils state of the art computing device studying and knowledge mining strategies
• info privacy-preserving facts mining equipment
This interdisciplinary source comprises process overview tables that permit for quick entry to universal cybersecurity difficulties and linked information mining equipment. quite a few illustrative figures aid readers visualize the workflow of advanced recommendations and greater than 40 case stories offer a transparent figuring out of the layout and alertness of knowledge mining and desktop studying recommendations in cybersecurity.
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Extra info for Data Mining and Machine Learning in Cybersecurity
141–152. , A. Schwartzbard, and M. Schatz. Learning program behavior profiles for intrusion detection USENIX Association. In: Proceedings of the 1st USENIX Workshop on Intrusion Detection and Network Monitoring, Santa Clara, CA, 1999. J. and J. Austin. A survey of outlier detection methodologies. Artificial Intelligence Review 22 (2) (2004): 85–126. Homeland Security Council. National Strategy for Homeland Security. 2007, p. H. R. Vemuri. Robust support vector machines for anomaly detection in computer security.
4. We address hybrid detection techniques in Chapter 5. 4 Scan Detection Scan detection generates alerts when attackers scan services or computer components in network systems before launching attacks. , destination IPs and the source IPs of Internet connections. Although many scan detection techniques have been proposed and declared to be able to detect the precursors of cyber attacks, the high false-positive rate or the low scan detection rate limits the application of these solutions in practice.
Amant. Building attack scenarios through integration of complementary alert correlation method. In: Proceedings of the 11th Annual Network and Distributed System Security Symposium, San Diego, CA, 2004. Patcha, A. M. Park. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks 51 (12) (2007): 3448–3470. A. G. Neumann. EMERALD: Event monitoring enabling responses to anomalous live disturbances. In: Proceedings of the Nineteenth Computer Security, Baltimore, MD, 1997, pp.
Data Mining and Machine Learning in Cybersecurity by Sumeet Dua, Xian Du