vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffb1362c000000751c060001000200 In the present era, the continued use of Entries on network systems has not only improved it but also posed many threats to its security. Most networks have a simple network attack such as phishing. These can be easily let go, and even hard attack developments like Ransomware occur on a few complex systems. Most attack events are caused by Entries coming in from outside the network. But sometimes the fact that networking takes place from within has added to the fear. Thus, it is intended to enhance the security improvements of that network. In this paper a machine learning algorithm designed with an advanced artificial intelligence is proposed. This method is designed to track Entries who log in to the network and some Entries who use the network's applications first. It has a security capability of 97.87% for dealing with phishing security threats, 98.88% for cyber data protection and 99.22% for ransomware controlled defenses. And its user approval is 96.58%. Thus this proposed method further enhances the key features of the security possibilities. This ensures maximum protection for Big Data modules and its storage systems from hackers and other security vulnerabilities.
J Gowthama Raja Kumaran, M Gayathri
Mahendra College of Engineering, India
Fishing Attacks, Ransomware Attacks, Machine Learning, Network Security, Data Protection, User Authentication
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 3 , Issue: 1 , Pages: 245-250 )
Date of Publication :
December 2021
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108
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