Automated anomaly detection in network management (Record no. 6121)

MARC details
000 -LEADER
fixed length control field 02156nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122841.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180605b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Shamil Sherhan (91616013)
9 (RLIN) 14332
245 ## - TITLE STATEMENT
Title Automated anomaly detection in network management
300 ## - PHYSICAL DESCRIPTION
Extent MSC MI 2016-2018
500 ## - GENERAL NOTE
General note <br/>Analyzing patterns from information stream and searching for anomalies will<br/>reveal the surprising in any quite business. For applications involving many information<br/>streams, the challenge of detecting anomalies has become tougher over time, as information<br/>will dynamically evolve in delicate ways in which following changes within the underlying<br/>infrastructure. As datasets increment in size and unpredictability, the human effort expected to<br/>look at dashboards or keep up rules for perceiving framework issues or business problems ends<br/>up impractical. The automated detection of potential business process anomalies could<br/>colossally help the business and different process members identify and comprehend the<br/>reasons for process errors. <br/><br/>In this project, an automated anomaly detector system is created using Robust <br/>Principal Component Analysis (RPCA) which identifies a low rank representation, random<br/>noise, and a set of outliers by repeatedly calculating the SVD and applying thresholds to the<br/>singular values and error for each iteration. In the network management system of the company,<br/>certain issue trackers called alarms arises whenever a network element is disturbed. In the<br/>current system, the alarms are handled based on the threshold in the network graph based on<br/>logs and inputs. Whenever a there is a spike or dip in the graph, they are marked as alarms and<br/>categorized based on the level of point they surpassed. In the proposed system, this technique<br/>will be replaced by a more enhanced and optimized technique which uses machine learning to<br/>find outliers by analyzing the log data received. <br/>
502 ## - DISSERTATION NOTE
Degree type MSC MI
Name of granting institution 2016-2018
Year degree granted INT
-- Dr. Asharaf S
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ANOMALY DETECTION
9 (RLIN) 14333
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element NETWORK MANAGEMENT
9 (RLIN) 14334
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
9 (RLIN) 14335
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   05/06/2018   R-1415 05/06/2018 05/06/2018 Project Reports