000			02156nam a22001937a 4500
003			OSt
005			20220107122841.0
008			180605b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aShamil Sherhan (91616013) _914332
245			_aAutomated anomaly detection in network management
300			_aMSC MI 2016-2018
500			_a Analyzing patterns from information stream and searching for anomalies will reveal the surprising in any quite business. For applications involving many information streams, the challenge of detecting anomalies has become tougher over time, as information will dynamically evolve in delicate ways in which following changes within the underlying infrastructure. As datasets increment in size and unpredictability, the human effort expected to look at dashboards or keep up rules for perceiving framework issues or business problems ends up impractical. The automated detection of potential business process anomalies could colossally help the business and different process members identify and comprehend the reasons for process errors. In this project, an automated anomaly detector system is created using Robust Principal Component Analysis (RPCA) which identifies a low rank representation, random noise, and a set of outliers by repeatedly calculating the SVD and applying thresholds to the singular values and error for each iteration. In the network management system of the company, certain issue trackers called alarms arises whenever a network element is disturbed. In the current system, the alarms are handled based on the threshold in the network graph based on logs and inputs. Whenever a there is a spike or dip in the graph, they are marked as alarms and categorized based on the level of point they surpassed. In the proposed system, this technique will be replaced by a more enhanced and optimized technique which uses machine learning to find outliers by analyzing the log data received.
502			_bMSC MI _c2016-2018 _dINT _eDr. Asharaf S
650			_aANOMALY DETECTION _914333
650			_aNETWORK MANAGEMENT _914334
650			_aMACHINE LEARNING _914335
942			_2ddc _cPR
999			_c6121 _d6121