MoEv - Revision history

SecurityAdm at 08:17, 28 November 2022

2022-11-28T08:17:24Z

SecurityAdm at 10:12, 9 November 2021

2021-11-09T10:12:46Z

SecurityAdm at 09:03, 4 November 2021

2021-11-04T09:03:31Z

SecurityAdm at 11:42, 28 October 2020

2020-10-28T11:42:06Z

SecurityAdm at 11:41, 28 October 2020

2020-10-28T11:41:33Z

SecurityAdm: Created page with "MoEV MoEv tool was used. MoEv is a general-purpose tool that allows for building classification models from labeled datasets moev. In addition, MoEv allows for performing da..."

2020-10-28T11:41:10Z

Created page with "MoEV MoEv tool was used. MoEv is a general-purpose tool that allows for building classification models from labeled datasets moev. In addition, MoEv allows for performing da..."

New page

MoEV

MoEv tool was used. MoEv is a general-purpose tool that allows for building classification models from labeled datasets moev. In addition, MoEv allows for performing data cleaning and pre-processing operations. It has been successfully used in different research areas such us jamming attacks detection on real-time location systems, academic success prediction at educational institutions or to detect network attacks.

The models that can be trained in MoEv are as follows:

{| class="wikitable" style="width: 300px; height: 50px; margin: 0 auto;"
|-
! Models
|-
| Adaptive Boosting
|-
| Bagging Classifier
|-
| Bernoulli Restricted Boltzmann Machine
|-
| Classification And Regression Tree
|-
| K-Nearest Neighbors
|-
| Linear Discriminant Analysis
|-
| Naive Bayes
|-
| One-vs-the-rest
|-
| Quadratic Discriminant Analysis
|-
| Random Forest
|-
| Stochastic Gradient Descent
|}

The internal scheme of MoEv is follows:

[[File:ComponentsDiagramMoEv.png|800px|frameless|center]]

Once the tool is used, the output that MoEv offers is similar to the following:

KNeighbors_Classifier.joblib

Accuracy for model is: '''0.964116'''

Classification report:

precision recall f1-score support

0 0.987845 0.942207 0.964486 198137
1 0.941017 0.987583 0.963738 184986

micro avg 0.964116 0.964116 0.964116 383123
macro avg 0.964431 0.964895 0.964112 383123
weighted avg 0.965235 0.964116 0.964125 383123

Confusion Matrix:

[[186686 11451]
[ 2297 182689]]

MoEv can be downloaded from the following link [https://niebla.unileon.es/cybersecurity/moev]

← Older revision		Revision as of 08:17, 28 November 2022
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−	MoEv is a general-purpose tool for building classification models from labeled datasets developed in Python. MoEv provides the following functionalities: data cleaning, normalization, dimensionality reduction, and hyperparameter optimization. This optimization is developed through the Grid-SearchCV method and also through DaskGridSearchCV. Dask GridSerarchCV provides advanced parallelism especially useful when using MoEv on a supercomputer. MoEv trains evaluate and obtain a report of supervised and semi-supervised/unsupervised learning models. The report includes highly relevant information such as Accuracy, Precision, Recall, F1-Score, and the confusion matrix. The latest release of the tool accepts as input CSV files or images.	+	MoEv is a general-purpose tool for building classification models from labeled datasets developed in Python. MoEv provides the following functionalities: data cleaning, normalization, dimensionality reduction, and hyperparameter optimization. This optimization is developed through the Grid-SearchCV method and also through DaskGridSearchCV. Dask GridSerarchCV provides advanced parallelism especially useful when using MoEv on a supercomputer. MoEv trains evaluate and obtain a report of supervised and semi-supervised/unsupervised learning models. The report includes highly relevant information such as Accuracy, Precision, Recall, F1-Score, FAR, and the confusion matrix. The latest release of the tool accepts as input CSV files or images.
	It has been successfully used in different research areas such as jamming attacks detection on real-time location systems, academic success prediction at educational institutions or to detect network attacks.		It has been successfully used in different research areas such as jamming attacks detection on real-time location systems, academic success prediction at educational institutions or to detect network attacks.

@@ Line 1: / Line 1: @@
-MoEv tool was used. MoEv is a general-purpose tool that allows for building classification models from labeled datasets moev. In addition, MoEv allows for performing data cleaning and pre-processing operations. It has been successfully used in different research areas such us jamming attacks detection on real-time location systems,  academic success prediction at educational institutions or to detect network attacks.
+MoEv is a general-purpose tool that allows for building classification models from labeled datasets MoEv. In addition, MoEv allows for performing data cleaning and pre-processing operations. It has been successfully used in different research areas such as jamming attacks detection on real-time location systems,  academic success prediction at educational institutions or to detect network attacks.
 The models that can be trained in MoEv are as follows:
@@ Line 34: / Line 34: @@
-The internal scheme of MoEv is follows:
+The internal scheme of MoEv is as follows:

← Older revision		Revision as of 11:42, 28 October 2020
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−	~~MoEV~~

@@ Line 66: / Line 66: @@
   [[186686  11451]
   [  2297 182689]]
 MoEv can be downloaded from the following link [https://niebla.unileon.es/cybersecurity/moev]