scholarly journals Network Performance Anomaly Detection and Localization

Author(s):  
P. Barford ◽  
N. Duffield ◽  
A. Ron ◽  
J. Sommers
Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1635
Author(s):  
Neeraj Chugh ◽  
Geetam Singh Tomar ◽  
Robin Singh Bhadoria ◽  
Neetesh Saxena

To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources.


2012 ◽  
Vol 3 (2) ◽  
pp. 13-33
Author(s):  
Robert Strahan

Communication is the lifeblood of any business. Today, communication is predominantly facilitated by digital packets transported over the interconnected arteries of the data network infrastructure. It is imperative that this infrastructure is well managed, that unexpected behavior is quickly identified and explained, and that problems are predicted and preempted. Therefore, network performance management systems should be able to detect unusual or anomalous behavior as it happens, and quickly trigger automatic analysis or alert a human operator. Growth trends in network traffic must also be identified so that future problems may be anticipated and prevented. To meet these challenges, this paper proposes an integrated, scalable method to perform baselining, anomaly detection, and forecasting on time series network metrics. The method is based on the popular Holt-Winters triple exponential smoothing technique – a technique that compares favorably to other more complex and costly approaches.


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