Malicious URL Website Detection using Selective Hyper Feature Link Stability based on Soft-Max Deep Featured Convolution Neural Network

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Proposed architecture diagram SHFLSR-SmDFCNN
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Published: Jun 13, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6s.6957
Keywords:
Malicious website Deep learning, Convolution neural network, feature selection, Link stability, Domain subsequent rate analysis, Hyper feature selection

Main Article Content

M. Pushpalatha
Ph.D. Research Scholar (PT), Assistant Professor, Department of Computer Science, Padmavani Arts & Science College for Women, Salem, TamilNadu.India - 636011
A. Vijaya
Research Supervisor, Assistant Professor & Head, Department of Computer Applications, Govt. Arts College (Autonomous), Salem-636 007, TamilNadu. India

Abstract

The web resource contains many domains with different users' Uniform Resource Locators (URLs). Due to the increasing amount of information on the Internet resource, malicious activities are done by hackers by expecting malicious websites in URL sub-links. Increasing information theft leads data sources to be vested in huge mediums. So, to analyze the web features to find the malicious webpage based on the deep learning approach, we propose a Selective Hyper Feature Link stability rate (SHFLSR) based on Soft-max Deep featured convolution neural network (SmDFCNN) for identifying the malicious website detection depends on the actions performed and its feature responses. Initially, the URL Signature Frame rate (USFR) is estimated to verify the domain-specific hosting. Then the link stability was confirmed by post-response rate using HyperLink stability post-response state (LSPRS). Depending upon the Spectral successive Domain propagation rate (S2DPR), the features were selected and trained with a deep neural classifier with a logically defined Softmax- Logical activator (SmLA) using Deep featured Convolution neural network (DFCNN). The proposed system performs a high-performance rate by detecting the malicious URL based on the behavioral response of the domain. It increases the detection rate, prediction rate, and classifier performance.

Article Details

How to Cite
Pushpalatha, M. ., & Vijaya, A. . (2023). Malicious URL Website Detection using Selective Hyper Feature Link Stability based on Soft-Max Deep Featured Convolution Neural Network. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6s), 490–498. https://doi.org/10.17762/ijritcc.v11i6s.6957
Issue
Vol. 11 No. 6s (2023): Advances in Computational Modeling and Simulation of Computing Systems
Section
Articles

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