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Data Mining for Intrusion Detection

Data Mining for Intrusion Detection

Aleksandar Lazarevic
Copyright: © 2008 |Pages: 8
ISBN13: 9781599049519|ISBN10: 1599049511|EISBN13: 9781599049526
DOI: 10.4018/978-1-59904-951-9.ch183
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MLA

Lazarevic, Aleksandar. "Data Mining for Intrusion Detection." Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, IGI Global, 2008, pp. 2880-2887. https://doi.org/10.4018/978-1-59904-951-9.ch183

APA

Lazarevic, A. (2008). Data Mining for Intrusion Detection. In J. Wang (Ed.), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 2880-2887). IGI Global. https://doi.org/10.4018/978-1-59904-951-9.ch183

Chicago

Lazarevic, Aleksandar. "Data Mining for Intrusion Detection." In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, 2880-2887. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-951-9.ch183

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Abstract

Today computers control power, oil and gas delivery, communication systems, transportation networks, banking and financial services, and various other infrastructure services critical to the functioning of our society. However, as the cost of the information processing and Internet accessibility falls, more and more organizations are becoming vulnerable to a wide variety of cyber threats. According to a recent survey by CERT/CC (Computer Emergency Response Team/Coordination Center), the rate of cyber attacks has been more than doubling every year in recent times (Figure 1). In addition, the severity and sophistication of the attacks are also growing. For example, Slammer/Sapphire Worm was the fastest computer worm in history. As it began spreading throughout the Internet, it doubled in size every 8.5 seconds and infected at least 75,000 hosts causing network outages and unforeseen consequences such as canceled airline flights, interference with elections, and ATM failures (Moore, 2003).

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