TITLE: A Novel Resampling Technique for Imbalanced Dataset Optimization 

ABSTRACT: Despite the enormous amount of data, particular events of interest can still
be quite rare. Classification of rare events is a common problem in many
domains, such as fraudulent transactions, malware traffic analysis and network
intrusion detection. Many studies have been developed for malware detection
using machine learning approaches on various datasets, but as far as we know
only the MTA-KDD'19 dataset has the peculiarity of updating the representative
set of malicious traffic on a daily basis. This daily updating is the added
value of the dataset, but it translates into a potential due to the class
imbalance problem that the RRw-Optimized MTA-KDD'19 will occur. We capture
difficulties of class distribution in real datasets by considering four types
of minority class examples: safe, borderline, rare and outliers. In this work,
we developed two versions of Generative Silhouette Resampling 1-Nearest
Neighbour (G1Nos) oversampling algorithms for dealing with class imbalance
problem. The first module of G1Nos algorithms performs a coefficient-based
instance selection silhouette identifying the critical threshold of Imbalance
Degree. (ID), the second module generates synthetic samples using a SMOTE-like
oversampling algorithm. The balancing of the classes is done by our G1Nos
algorithms to re-establish the proportions between the two classes of the used
dataset. The experimental results show that our oversampling algorithm work
better than the other two SOTA methodologies in all the metrics considered.
Despite the enormous amount of data, particular events of interest can still
be quite rare. Classification of rare events is a common problem in many
domains, such as fraudulent transactions, malware traffic analysis and network
intrusion detection. Many studies have been developed for malware detection
using machine learning approaches on various datasets, but as far as we know
only the MTA-KDD'19 dataset has the peculiarity of updating the representative
set of malicious traffic on a daily basis. This daily updating is the added
value of the dataset, but it translates into a potential due to the class
imbalance problem that the RRw-Optimized MTA-KDD'19 will occur. We capture
difficulties of class distribution in real datasets by considering four types
of minority class examples: safe, borderline, rare and outliers. In this work,
we developed two versions of Generative Silhouette Resampling 1-Nearest
Neighbour (G1Nos) oversampling algorithms for dealing with class imbalance
problem. The first module of G1Nos algorithms performs a coefficient-based
instance selection silhouette identifying the critical threshold of Imbalance
Degree. (ID), the second module generates synthetic samples using a SMOTE-like
oversampling algorithm. The balancing of the classes is done by our G1Nos
algorithms to re-establish the proportions between the two classes of the used
dataset. The experimental results show that our oversampling algorithm work
better than the other two SOTA methodologies in all the metrics considered.
A Novel Resampling Technique for Imbalanced Dataset Optimization
http://arxiv.org/abs/2012.15231v1