Authors :
Dr. Narasimha Chary CH; Dr. Srihari Chintha; E.Rajendra; Dr. Sunke Srinivas
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://t.ly/K3QJ
DOI :
https://doi.org/10.5281/zenodo.8012114
Abstract :
The development of real-world
databases presents computing difficulties for a
single computer. Cloud-based systems, on the
other hand, can handle massive quantities of data
management activities for large-scale real-world
data set calculations. The study focuses on a new
Generalized Flow inside the cloud computing
platform, Microsoft Azure Machine Learning
Studio (MAMLS), which analyses multi-class and
binary classification data sets to maximise overall
classification accuracy. To begin, each data set is
split into training and testing sets. Following that,
the training data is utilised to create
classification model parameters. Reduce the
dimensionality of your data to enhance
classification accuracy. Data-centered
information increases overall classification
accuracy by reducing multi-class classification to
a series of hierarchical binary classification
problems. Finally, the performance of the
improved classification model is tested and
appraised. The proposed study assessed
algorithm performance utilising 82,332 test
samples from a recent data set, UNSW NB-15. It
took 6 seconds to train 1,75,341 network
instances using the suggested two-class forest
decision model. At 99 percent, 94.49 percent,
91.79 percent, and 90.9 percent, the multi-level
forest decision- making model recognised attack
types such as generics, feats, shellcodes, and
worms, respectively.
Keywords :
Azure Machine Learning; Decision Forest; Intrusion Detection; Locally Deep SVM; Mutual Information; UNSW NB-15.
The development of real-world
databases presents computing difficulties for a
single computer. Cloud-based systems, on the
other hand, can handle massive quantities of data
management activities for large-scale real-world
data set calculations. The study focuses on a new
Generalized Flow inside the cloud computing
platform, Microsoft Azure Machine Learning
Studio (MAMLS), which analyses multi-class and
binary classification data sets to maximise overall
classification accuracy. To begin, each data set is
split into training and testing sets. Following that,
the training data is utilised to create
classification model parameters. Reduce the
dimensionality of your data to enhance
classification accuracy. Data-centered
information increases overall classification
accuracy by reducing multi-class classification to
a series of hierarchical binary classification
problems. Finally, the performance of the
improved classification model is tested and
appraised. The proposed study assessed
algorithm performance utilising 82,332 test
samples from a recent data set, UNSW NB-15. It
took 6 seconds to train 1,75,341 network
instances using the suggested two-class forest
decision model. At 99 percent, 94.49 percent,
91.79 percent, and 90.9 percent, the multi-level
forest decision- making model recognised attack
types such as generics, feats, shellcodes, and
worms, respectively.
Keywords :
Azure Machine Learning; Decision Forest; Intrusion Detection; Locally Deep SVM; Mutual Information; UNSW NB-15.
