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:  Mixed type data 
1. Abstract of Cited Paper (BIB001):  Outlier detection has attracted substantial attention in many applications and research areas; some of the most prominent applications are network intrusion detection or credit card fraud detection. Many of the existing approaches are based on calculating distances among the points in the dataset. These approaches cannot easily adapt to current datasets that usually contain a mix of categorical and continuous attributes, and may be distributed among different geographical locations. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose a fast distributed outlier detection strategy intended for datasets containing mixed attributes. The proposed method takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and the number of attributes in the dataset. Experimental results show that the proposed outlier detection method compares very favorably with other state-of-the art outlier detection strategies proposed in the literature and that the speedup achieved by its distributed version is very close to linear.

Literature Review:
Handling mixed attribute data in LOADED BIB001 . Discriminate outliers for categorical and continuous attributes in RELOADED  . Continuous score finding in ODMAD BIB002 for mixed attribute data. normal or abnormal. These approaches can be used for on-line classification, where the classifier learns the classification model and then classifies new exemplars as and when required against the learned model. If the new exemplar lies in a region of normality it is classified as normal, otherwise it is flagged as an outlier. Classification algorithms require a good spread of both normal and abnormal labelled data.