By Florez G., Bridges S.M., Vaughn R.B.
Now we have been utilizing fuzzy information mining innovations to extract styles that characterize general habit for intrusion detection. during this paper we describe a number of adjustments that we've got made to the information mining algorithms so one can enhance accuracy and potency. We use units of fuzzy organization ideas which are mined from community audit info as types of "normal habit. To notice anomalous habit, wegenerate fuzzy organization principles from new audit facts and compute the similarity with units mined from "normal" info. If the similarity values are less than a threshold price, an alarm isissued. during this paper we describe an set of rules for computing fuzzy organization ideas according to Borgelt's prefix timber, transformations to the computation of help and self assurance offuzzy ideas, a brand new procedure for computing the similarity of 2 fuzzy rule units, and have choice and optimization with genetic algorithms. Experimental effects reveal that we will in achieving greater working time and accuracy with those transformations.
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Additional info for An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection
Chapter 4 Arithmetic Compression Chapter 2 describes how to use a statistical model of the data to create variablelength codes for a ˇ le. The most common patterns or words get short codes, and the others get longer codes. The Huffman coding algorithms described in that chapter make a good introduction to the process, but they are not optimal. Arithmetic coding is a similar class of algorithms that can squeeze the data even more. The basic approach is very similar, but it ˇ nds the extra space to compress between the characters.
If a string has a previous occurrence in the ˇ le, it is replaced with a tag containing a distance (less than 32k) and a length of the string (258 character maximum). The algorithm uses two Huffman trees to add additional compression. One is used to handle the literal strings present on the ˇ rst occurrence as well as the lengths of the strings. Another tree is used to compress the distance, and this helps optimize patterns. The hash table is limited to three characters at the start of the string.
The simplest approach is to keep the tree in memory and walk down the tree pulling bits off as you go. This process can be slow if it is done bit by bit. The process can be accelerated by aggregating the lookup for the ˇ rstseveral bits. A table created with 2n entries for the ˇ rst n bits will speed things up. 2 Shannon-Fano Encoding The Shannon-Fano compression algorithm is a close cousin to the Huffman solution. Both systems begin by creating one node for each character, but they build the tree differently.
An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection by Florez G., Bridges S.M., Vaughn R.B.