@Article{2018.100000050,
AUTHOR = {Jianfeng Guan, Jiawei Li, Zhongbai Jiang},
TITLE = {The Design and Implementation of a Multidimensional and Hierarchical  Web Anomaly Detection System},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {1},
PAGES = {131--141},
URL = {http://www.techscience.com/iasc/v25n1/39641},
ISSN = {2326-005X},
ABSTRACT = {The traditional web anomaly detection systems face the challenges derived from the 
constantly evolving of the web malicious attacks, which therefore result in high false 
positive rate, poor adaptability, easy over-fitting, and high time complexity. Due to 
these limitations, we need a new anomaly detection system to satisfy the 
requirements of enterprise-level anomaly detection. There are lots of anomaly 
detection systems designed for different application domains. However, as for web 
anomaly detection, it has to describe the network accessing behaviours characters 
from as many dimensions as possible to improve the performance. In this paper we 
design and implement a Multidimensional and Hierarchical Web Anomaly Detection 
System (MHWADS) with the objectives to provide high performance, low latency, 
multi-dimension and adaptability. MHWADS calculates the statistical characteristics, 
and constructs the corresponding statistical model, detects the behaviour 
characteristics to generate the multidimensional correlation eigenvectors, and 
adopts several classifications to build an ensemble model. The system performance 
is evaluated based on realistic dataset, and the experimental results show that 
MHWADS yields substantial improvements than the previous single model. More 
important, by using 2-fold Stacking as the ensemble architecture, the detection 
precision and recall are 0.99988 and 0.99647, respectively.},
DOI = {10.31209/2018.100000050}
}