Reference no: EM131008732 , Length: 10 slides

Message : Computing case study with two tasks.

Task 1 is analyse a pcap file and identify what causing the incident.

Task 2 is develop an IDS with the use of the material provided. The ids must detect as many attacks as possible and you must report them in a human readable format.

Preprocessing

discussion techniques can be used to reduce the number of values of the given continuous attribute by dividing the range of attributes into intervals. Interval labels can then be used to replace actual data values
especially important in decision tree methods of classification, where data mining is applied to preprocess data. These methods are typically recursive, were large amount of time is spent on sorting the data at each step. Hence, the smaller the number of distinct values to sort the faster these methods should be many discussion techniques can be applied recursively in order to provide a hierarchical multiresolution partitioning of the attributes values known as concept hierarchys.

Kdd

Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), a field at the intersection of computer science and statistics is the process that attempts to discover patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.