NDLI: Incremental Local Outlier Detection for Data Streams

Content Provider	IEEE Xplore Digital Library
Author	Pokrajac, D. Lazarevic, A. Latecki, L.J.
Copyright Year	2007
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Delaware State Univ., Dover, DE (Pokrajac, D.)
Abstract	Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outliers have to be detected from data streams that arrive at an enormous pace. In this paper, an incremental LOF (local outlier factor) algorithm, appropriate for detecting outliers in data streams, is proposed. The proposed incremental LOF algorithm provides equivalent detection performance as the iterated static LOF algorithm (applied after insertion of each data record), while requiring significantly less computational time. In addition, the incremental LOF algorithm also dynamically updates the profiles of data points. This is a very important property, since data profiles may change over time. The paper provides theoretical evidence that insertion of a new data point as well as deletion of an old data point influence only limited number of their closest neighbors and thus the number of updates per such insertion/deletion does not depend on the total number of points TV in the data set. Our experiments performed on several simulated and real life data sets have demonstrated that the proposed incremental LOF algorithm is computationally efficient, while at the same time very successful in detecting outliers and changes of distributional behavior in various data stream applications
Starting Page	504
Ending Page	515
File Size	15109846
Page Count	12
File Format	PDF
ISBN	1424407052
DOI	10.1109/CIDM.2007.368917
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Event detection Change detection algorithms Intrusion detection Telecommunication traffic Traffic control Video surveillance Computer networks Computational Intelligence Society Data mining Unsupervised learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Detection of Local Outlier over Dynamic Data Streams Using Efficient Partitioning Method

Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection

Hybrid Classifier Systems for Intrusion Detection

Behavior Analysis-Based Learning Framework for Host Level Intrusion Detection

Optimal Evaluation of Feature Selection in Intrusion Detection Modeling

An Adaptive Clustering Algorithm for Intrusion Detection

Online Reactive Anomaly Detection over Stream Data

Resource-sensitive intrusion detection models for network traffic

ARTIFICIAL INTELLIGENCE APPROACHES FOR INTRUSION DETECTION

Incremental Local Outlier Detection for Data Streams

Similar Documents

Detection of Local Outlier over Dynamic Data Streams Using Efficient Partitioning Method

Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection

Hybrid Classifier Systems for Intrusion Detection

Behavior Analysis-Based Learning Framework for Host Level Intrusion Detection

Optimal Evaluation of Feature Selection in Intrusion Detection Modeling

An Adaptive Clustering Algorithm for Intrusion Detection

Online Reactive Anomaly Detection over Stream Data

Resource-sensitive intrusion detection models for network traffic

ARTIFICIAL INTELLIGENCE APPROACHES FOR INTRUSION DETECTION

Incremental Local Outlier Detection for Data Streams