scholarly journals Impact of Feature Selection Methods on the Classification of DDoS Attacks using XGBoost

2021 ◽  
Vol 36 (1) ◽  
pp. 200-214
Author(s):  
Pedro Araujo ◽  
Anderson Silva ◽  
Norisvaldo Junior ◽  
Fabio Cabrini ◽  
Alessandro Santos ◽  
...  
Keyword(s):  
Feature Selection ◽  
Ddos Attacks ◽  
Selection Methods

scholarly journals BETTER ALTERNATIVES FOR STEPWISE DISCRIMINANT ANALYSIS

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Katarzyna Stąpor
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Tabu Search ◽  
Stepwise Discriminant Analysis ◽  
Selection Methods ◽  
Discrimination Power ◽  
Statistical Software ◽  
Software Packages ◽  
Benchmark Datasets

Discriminant Analysis can best be defined as a technique which allows the classification of an individual into several dictinctive populations on the basis of a set of measurements. Stepwise discriminant analysis (SDA) is concerned with selecting the most important variables whilst retaining the highest discrimination power possible. The process of selecting a smaller number of variables is often necessary for a variety number of reasons. In the existing statistical software packages SDA is based on the classic feature selection methods. Many problems with such stepwise procedures have been identified. In this work the new method based on the metaheuristic strategy tabu search will be presented together with the experimental results conducted on the selected benchmark datasets. The results are promising.

Efficient Classification of DDoS Attacks Using an Ensemble Feature Selection Algorithm

2017 ◽  
Vol 29 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Khundrakpam Johnson Singh ◽  
Tanmay De
Keyword(s):  
Feature Selection ◽  
Denial Of Service ◽  
Computation Time ◽  
Threshold Value ◽  
Ddos Attacks ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Cyber Threats ◽  
The Given

Abstract In the current cyber world, one of the most severe cyber threats are distributed denial of service (DDoS) attacks, which make websites and other online resources unavailable to legitimate clients. It is different from other cyber threats that breach security parameters; however, DDoS is a short-term attack that brings down the server temporarily. Appropriate selection of features plays a crucial role for effective detection of DDoS attacks. Too many irrelevant features not only produce unrelated class categories but also increase computation overhead. In this article, we propose an ensemble feature selection algorithm to determine which attribute in the given training datasets is efficient in categorizing the classes. The result of the ensemble algorithm when compared to a threshold value will enable us to decide the features. The selected features are deployed as training inputs for various classifiers to select a classifier that yields maximum accuracy. We use a multilayer perceptron classifier as the final classifier, as it provides better accuracy when compared to other conventional classification models. The proposed method classifies the new datasets into either attack or normal classes with an efficiency of 98.3% and also reduces the overall computation time. We use the CAIDA 2007 dataset to evaluate the performance of the proposed method using MATLAB and Weka 3.6 simulators.

The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures

2017 ◽  
Vol 222 ◽  
pp. 49-56 ◽  
Author(s):  
Lucas R. Trambaiolli ◽  
Claudinei E. Biazoli ◽  
Joana B. Balardin ◽  
Marcelo Q. Hoexter ◽  
João R. Sato
Keyword(s):  
Feature Selection ◽  
Obsessive Compulsive Disorder ◽  
Selection Methods ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Volumetric Measures
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