چکیده (انگلیسی):

In contrast to traditional Intrusion Detection Systems
(IDSs), data mining anomaly detection methods/techniques has been
widely used in the domain of network traffic data for intrusion
detection and cyber threat. Data mining is widely recognized as
popular and important intelligent and automatic tools to assist
humans in big data security analysis and anomaly detection over
IDSs. In this study we discuss our review in data mining anomaly
detection methods for HTTP web services. Today, many online
careers and actions including online shopping and banking are
running through web-services. Consequently, the role of Hypertext
Transfer Protocol (HTTP) in web services is crucial, since it is the
standard facilitator for communication protocol. Hence, among the
intruders that bound attacks, HTTP is being considered as a vital
middle objective. In the recent years, an effective system that has
attracted the attention of the researchers is the anomaly detection
which is based on data mining methods. We provided an overview on
four general data mining techniques such as classification, clustering,
semi-supervised and association rule mining. These data mining
anomaly detection methods can be used to computing intelligent HTTP
request data, which are necessary in describing user behavior. To meet
the challenges of data mining techniques, we provide challenges and
issues section for intrusion detection systems in HTTP web services.