Authors :
Dilip Dalgade; Srushti Patyane; Anushka Matey; Saloni Singh; Amey Godbole
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/4s8w5dee
Scribd :
https://tinyurl.com/4r93n493
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1102
Abstract :
As the level of malware and viruses is on the
rise, the prominence of effective detection systems is
crucial. Malwares are the modern-day threats that have
troubled major companies worldwide. This article
explores in depth two powerful machine learning tools,
Random Forest, Support Vector Machines in particular,
for the detection of malware. Our study revealed the
Random Forest's capacity to reach the upper detection
accuracy limit of 98% by applying an analysis of a dataset
of variousmalware samples. The feature selection process
as well as the model improvement that we've adopted
have substantially improved use of our approach for
malware detection, and this is thereby highly crucial for
organizations to fight against evolving cyber threats. The
results of the present research support the ongoing
actionsof strengthening cybersecurity security, therefore,
providing invaluable information for proactive defense
approach mechanisms against malicious software
attacks.
Keywords :
Malware, Machine Learning, Random Forest, Support Vector Machines (SVM), Detection Accuracy, Cybersecurity, Feature Selection, Model Optimization.
As the level of malware and viruses is on the
rise, the prominence of effective detection systems is
crucial. Malwares are the modern-day threats that have
troubled major companies worldwide. This article
explores in depth two powerful machine learning tools,
Random Forest, Support Vector Machines in particular,
for the detection of malware. Our study revealed the
Random Forest's capacity to reach the upper detection
accuracy limit of 98% by applying an analysis of a dataset
of variousmalware samples. The feature selection process
as well as the model improvement that we've adopted
have substantially improved use of our approach for
malware detection, and this is thereby highly crucial for
organizations to fight against evolving cyber threats. The
results of the present research support the ongoing
actionsof strengthening cybersecurity security, therefore,
providing invaluable information for proactive defense
approach mechanisms against malicious software
attacks.
Keywords :
Malware, Machine Learning, Random Forest, Support Vector Machines (SVM), Detection Accuracy, Cybersecurity, Feature Selection, Model Optimization.
