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Title: | ANOMALY DETECTION TECHNIQUES |
Authors: | MITTAL, SHREEYA |
Keywords: | INTRUSION DETECTION SYSTEM (IDS) DATA MINING TECHNIQUES ANOMALY DETECTION HYBRID APPROACHES |
Issue Date: | Oct-2020 |
Publisher: | DELHI TECHNOLOGICAL UNIVERSITY |
Series/Report no.: | TD - 5352; |
Abstract: | In the present world huge amounts of data are stored and transferred from one location to another. The data when transferred or stored is primed exposed to attack. Although various techniques or applications are available to protect data, loopholes exist. The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security – especially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. Thus to analyze data and to determine various kind of attack, data mining techniques have emerged to make it less vulnerable. Anomaly detection uses these data mining techniques to detect the surprising behavior hidden within data increasing the chances of being intruded or attacked. Various hybrid approaches have also been made in order to detect known and unknown attacks more accurately. This paper presents a number of anomaly detection techniques that have been presented by various researchers. |
URI: | http://dspace.dtu.ac.in:8080/jspui/handle/repository/18820 |
Appears in Collections: | M.E./M.Tech. Computer Engineering |
Files in This Item:
File | Description | Size | Format | |
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Shreeya Thesis.pdf | 770.64 kB | Adobe PDF | View/Open |
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