DeepAI
Log In Sign Up

Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

01/30/2018
by   James Zhang, et al.
0

Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT) and Simple Feedforward Neural Network. The effectiveness of each algorithm was evaluated and compared. Both have shown sufficient performance and sensitivity.

READ FULL TEXT

page 2

page 4

05/28/2020

Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents

This paper proposes an anomaly detection method for the prevention of in...
12/21/2020

Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape

Anomaly detection aims at identifying unexpected fluctuations in the exp...
11/29/2018

A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics

In this paper we propose a novel machine-learning method for anomaly det...
01/05/2022

Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation

The emergence of new nanoscale technologies has imposed significant chal...
10/22/2021

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

High-power particle accelerators are complex machines with thousands of ...
07/30/2021

Extracting Grammars from a Neural Network Parser for Anomaly Detection in Unknown Formats

Reinforcement learning has recently shown promise as a technique for tra...