You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Outlier Detection Methods in Network Anomaly Identification 
Chapter Title:  Kernel Function using Fuzzy Approach 
1. Abstract of Cited Paper (BIB001):  The paper discusses outlier detection algorithms used in data mining systems. Basic approaches currently used for solving this problem are considered, and their advantages and disadvantages are discussed. A new outlier detection algorithm is suggested. It is based on methods of fuzzy set theory and the use of kernel functions and possesses a number of advantages compared to the existing methods. The performance of the algorithm suggested is studied by the example of the applied problem of anomaly detection arising in computer protection systems, the so-called intrusion detection systems.

Literature Review:
This is a fuzzy clustering method in the feature space for the multi-class classification problem. The approach BIB002 searches for one common cluster containing images of all objects from the original space. In this case, the membership degree of an object image with respect to the fuzzy cluster in the feature space may be viewed as a typicalness degree of the object, i.e., a measure opposed to outlierness. Objects with a low typicalness degree (less than a threshold determined by the user) are considered outliers. It should be noted that the modification of the threshold (i.e., the modification of the outlier factor criterion) does not require model reconstruction, which is the case when the distancebased algorithms are used. Petrovskiy introduces a fuzzy kernel-based method for real-time network intrusion detection BIB001 . It involves a kernel-based fuzzy clustering technique. Here, network audit records are vectors with numeric and nominal attributes. These vectors are implicitly mapped by means of a special kernel function into a high dimensional feature space, where the possibilistic clustering algorithm is applied to calculate the measure of typicalness and to discover outliers.