Data mining methods for anomaly detection KDD-2005 workshop report

ABSTRACT

For many applications, data mining systems are required to detect anomalous (abnormal, unmodeled, or unexpected) observations. This has so far proven to be a difficult challenge because anomalies are usually considered to be "non-normal" observations, where "normality" is typically defined by very complex concepts. Because of these and other reasons, there are no standard and principled approaches for anomaly detection, yet, and the data mining processes that have led to successful solutions include most of the times ad-hoc (algorithmic, design, and implementation) decisions that incorporate prior or commonsense knowledge about the tasks that are addressed.Consequently, we considered that it would be beneficial for both researchers and practitioners interested in anomaly detection and data mining, to organize workshop that would bring together people interested in this topic. We considered that the International Conference on Knowledge Discovery and Data Mining would be a good venue for such a workshop because of the diversity of interests, backgrounds, and problems that motivate people to attend the conference.This paper describes the workshop on "Data Mining Methods for Anomaly Detection" - a one day event held in conjunction with KDD-2005 in Chicago, on August 21, 2005.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration
          Subjects: Security, integrity, and protection

Additional Classification:
  A. General Literature
  A.1 INTRODUCTORY AND SURVEY

  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Concept learning

General Terms:
Documentation

Keywords:
anomalies, data mining, detection of anomalies in data, machine learning