A Survey of Outlier Detection Methods in Network Anomaly Identification <s> Soft computing Method -Roughset Approach <s> Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition. <s> BIB001 </s> A Survey of Outlier Detection Methods in Network Anomaly Identification <s> Soft computing Method -Roughset Approach <s> The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy. <s> BIB002
The rough set BIB001 philosophy is based on the assumption that with every object of the universe there is associated a certain amount of information (data, knowledge), expressed by means of its attributes. Objects having the same description are indiscernible. The basic idea of rough sets is discussed in Sub-section 2.3. In BIB002 , RST (Rough Set Theory) and SVM (Support Vector Machine) are used to detect intrusions. First, RST is used to preprocess the data and to reduce the dimensions. Next, the features selected by RST are sent to the SVM model to learn and test respectively. The method is effective in decreasing the space density of data and has low false positive rate and good accuracy of detection. The major advantage of rough set theory is that it does not need any preliminary or additional information about data, such as a probability distribution. The main problems that can be solved using rough set theory include data reduction (i.e., elimination of superfluous data), discovery of data dependencies, estimation of data significance, generation of decision (control) algorithms from data, approximate classification of data, discovery of similarities or differences in data, discovery of patterns in data, and discovery of causeeffect relationships.