NDLI: Anomaly intrusion detection based upon data mining techniques and fuzzy logic

Content Provider	IEEE Xplore Digital Library
Author	Yingbing Yu Han Wu
Copyright Year	2012
Description	Author affiliation: Dept. of Mathematics & Statistics, Minnesota State University, Mankato, Minnesota, USA (Han Wu) \|\| Dept. of Computer Science & Information Technology, Austin Peay State University, Clarksville, Tennessee, USA (Yingbing Yu)
Abstract	Intrusion detection systems (IDSs) attempt to identify attacks by comparing new data to predefined signatures known to be malicious (misuse IDSs) or to a model of normal behavior (anomaly-based IDSs). Anomaly intrusion detection approaches have the advantage of detecting previously unknown or new attacks, but suffer from the possible high false alarms due to the problem of behavior drifting and the difficulty of building an adaptive model. In this paper, we propose a model based on the data mining technique - naïve Bayes classification to classify an input event (system call sequences generated from privileged processes) as “normal” or “anomalous” to detect system anomalous behavior. The independent frequency of each system call from a process collected under the normal conditions is the basis for the classifier. The ratio of the probability of a sequence from a process and the probability NOT from the process serves as the input of a fuzzy system for the classification. Experimental results in a data set consisting of both normal and intrusion traces show that the model can successfully detect most of intrusion traces with a very low false alarm rate.
Starting Page	514
Ending Page	517
File Size	246749
Page Count	4
File Format	PDF
ISBN	9781467317139
e-ISBN	9781467317146
e-ISBN	9781467317122
DOI	10.1109/ICSMC.2012.6377776
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-14
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intrusion detection Hidden Markov models Data mining Data models Monitoring Training data Fuzzy logic Privileged Processes Anomaly Intrusion Detection Data Mining Naïve Bayes Classifiers Fuzzy Logic System Calls
Content Type	Text
Resource Type	Article

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