Manuscript Title:

DETECTION OF MALWARE USING MACHINE LEARNING TECHNIQUES

Author:

HARITHA RAJEEV, Dr. MIDHUN CHAKKARAVARTY

DOI Number:

DOI:10.17605/OSF.IO/QYVAH

Published : 2023-04-10

About the author(s)

1. HARITHA RAJEEV - Research scholar, Department of Information Technology, Lincoln University College, Malaysia.
2. Dr. MIDHUN CHAKKARAVARTY - Assistant Professor, Department of Information Technology, Lincoln University College, Malaysia.

Full Text : PDF

Abstract

An application created specifically to infiltrate and harm PCs without the owner's consent is known as a malicious PC programme. Malware can take on many different shapes, such as infections, rootkits, key loggers, worms, Trojan horses, spyware, ransomware, secondary gateways, bots, logic bombs, and so forth. The amount, variety, and speed of non-PC applications' transmission are all steadily growing. The advancement of exchanging frameworks is a result of neural network concept, which has its roots in computerized reasoning. In the field of financial industry, the neural network application performs different roles like time series forecasting, algorithmic trading, securities classification, credit risk modelling, and the production of ownership indicators and exit prices, among others. The vector component and the stage delivery computation both have an impact. They also coordinate both include based on substance and include based on behavior. They put out the SVM-AR integrated learning approach, which combines hierarchical principles and a vector backing machine.


Keywords

Random forests, Logistic Regression, Neural Network.