MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement
Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is...
Gespeichert in:
Veröffentlicht in: | Cluster computing 2024-08, Vol.27 (5), p.6377-6395 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 6395 |
---|---|
container_issue | 5 |
container_start_page | 6377 |
container_title | Cluster computing |
container_volume | 27 |
creator | Yamarthy, Anil Kumar Koteswararao, Ch |
description | Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is phished or non-phished. Some anti-phishing software is used to identify the phishing data. However, few of these techniques did not provide efficient performance. Hence, the proposed model is introduced to overcome the issues obtained and improve the efficiency of detecting whether the data is phished or non-phished. The data is gathered from the Phishstorm dataset, which is pre-processed using the Z score normalization method and data cleaning. Data balancing is done by the Advanced synthetic sampling approach (Adv-SyN) to balance the dataset, and the features are extracted using a Double self-sparse autoencoder (DSelSa). The Opposition Gazelle optimization algorithm (OpGoA) model is used for optimal feature selection, and finally, the data is classified using Multi Head Depth wise Tern integrated long short term memory (MDepthNet). The sooty tern optimization is used to evaluate the loss function of the network model. The performance of the proposed model is analyzed based on some evaluation metrics and compared with other models, which describes the efficiency of the proposed model. The main objective of proposed technique used to detect the phishing attack and phishing or non-phishing. An automated DL methodology introduced for effective detection of phishing attacks for enhancing the cyber security. The accuracy of the proposed model is obtained as 99.45%, and Precision is 99.45%. RMSE and MSE rate of the proposed model is reduced to 0.73 and 0.05 for better performance. |
doi_str_mv | 10.1007/s10586-024-04313-w |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3092151767</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3092151767</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-1094f619eca883b02df6092d4593857e7f6dc03a59857b8ff47e0e2b5e73feca3</originalsourceid><addsrcrecordid>eNp9UMlOwzAQtRBIlMIPcLLEOWDHcZwcUVmlAhc4W44zTlJaO9iOqv49LkXixmmWt4zmIXRJyTUlRNwESnhVZiQvMlIwyrLtEZpRLlgmeMGOU88SLCouTtFZCCtCSC3yeoamlzsYY_8KETcqQIvHfgj9YDusYlT6E7cQQcfBWTyF_XqwETqvYqK2ACNeg_J2D2wg9q51a9cNELBxHutdAx4H0JMf4g6D7ZXVsAEbz9GJUesAF791jj4e7t8XT9ny7fF5cbvMNKN1zCipC1PSGrSqKtaQvDUlqfO24DVLr4AwZasJU7xOU1MZUwggkDccBDNJxObo6uA7evc1QYhy5SZv00nJkhHlVJQisfIDS3sXggcjRz9slN9JSuQ-XnmIV6Z45U-8cptE7CAKiWw78H_W_6i-AUl4gKg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3092151767</pqid></control><display><type>article</type><title>MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement</title><source>SpringerLink Journals - AutoHoldings</source><creator>Yamarthy, Anil Kumar ; Koteswararao, Ch</creator><creatorcontrib>Yamarthy, Anil Kumar ; Koteswararao, Ch</creatorcontrib><description>Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is phished or non-phished. Some anti-phishing software is used to identify the phishing data. However, few of these techniques did not provide efficient performance. Hence, the proposed model is introduced to overcome the issues obtained and improve the efficiency of detecting whether the data is phished or non-phished. The data is gathered from the Phishstorm dataset, which is pre-processed using the Z score normalization method and data cleaning. Data balancing is done by the Advanced synthetic sampling approach (Adv-SyN) to balance the dataset, and the features are extracted using a Double self-sparse autoencoder (DSelSa). The Opposition Gazelle optimization algorithm (OpGoA) model is used for optimal feature selection, and finally, the data is classified using Multi Head Depth wise Tern integrated long short term memory (MDepthNet). The sooty tern optimization is used to evaluate the loss function of the network model. The performance of the proposed model is analyzed based on some evaluation metrics and compared with other models, which describes the efficiency of the proposed model. The main objective of proposed technique used to detect the phishing attack and phishing or non-phishing. An automated DL methodology introduced for effective detection of phishing attacks for enhancing the cyber security. The accuracy of the proposed model is obtained as 99.45%, and Precision is 99.45%. RMSE and MSE rate of the proposed model is reduced to 0.73 and 0.05 for better performance.</description><identifier>ISSN: 1386-7857</identifier><identifier>EISSN: 1573-7543</identifier><identifier>DOI: 10.1007/s10586-024-04313-w</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Algorithms ; Classification ; Computer Communication Networks ; Computer Science ; Confidentiality ; Cybercrime ; Cybersecurity ; Datasets ; Deep learning ; Digitization ; Electronic mail systems ; Fraud ; Internet ; Machine learning ; Malware ; Methods ; Neural networks ; Operating Systems ; Optimization ; Optimization algorithms ; Performance evaluation ; Personal information ; Phishing ; Processor Architectures ; Websites</subject><ispartof>Cluster computing, 2024-08, Vol.27 (5), p.6377-6395</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-1094f619eca883b02df6092d4593857e7f6dc03a59857b8ff47e0e2b5e73feca3</citedby><cites>FETCH-LOGICAL-c319t-1094f619eca883b02df6092d4593857e7f6dc03a59857b8ff47e0e2b5e73feca3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10586-024-04313-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10586-024-04313-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Yamarthy, Anil Kumar</creatorcontrib><creatorcontrib>Koteswararao, Ch</creatorcontrib><title>MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement</title><title>Cluster computing</title><addtitle>Cluster Comput</addtitle><description>Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is phished or non-phished. Some anti-phishing software is used to identify the phishing data. However, few of these techniques did not provide efficient performance. Hence, the proposed model is introduced to overcome the issues obtained and improve the efficiency of detecting whether the data is phished or non-phished. The data is gathered from the Phishstorm dataset, which is pre-processed using the Z score normalization method and data cleaning. Data balancing is done by the Advanced synthetic sampling approach (Adv-SyN) to balance the dataset, and the features are extracted using a Double self-sparse autoencoder (DSelSa). The Opposition Gazelle optimization algorithm (OpGoA) model is used for optimal feature selection, and finally, the data is classified using Multi Head Depth wise Tern integrated long short term memory (MDepthNet). The sooty tern optimization is used to evaluate the loss function of the network model. The performance of the proposed model is analyzed based on some evaluation metrics and compared with other models, which describes the efficiency of the proposed model. The main objective of proposed technique used to detect the phishing attack and phishing or non-phishing. An automated DL methodology introduced for effective detection of phishing attacks for enhancing the cyber security. The accuracy of the proposed model is obtained as 99.45%, and Precision is 99.45%. RMSE and MSE rate of the proposed model is reduced to 0.73 and 0.05 for better performance.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Classification</subject><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Confidentiality</subject><subject>Cybercrime</subject><subject>Cybersecurity</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Digitization</subject><subject>Electronic mail systems</subject><subject>Fraud</subject><subject>Internet</subject><subject>Machine learning</subject><subject>Malware</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Operating Systems</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Performance evaluation</subject><subject>Personal information</subject><subject>Phishing</subject><subject>Processor Architectures</subject><subject>Websites</subject><issn>1386-7857</issn><issn>1573-7543</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9UMlOwzAQtRBIlMIPcLLEOWDHcZwcUVmlAhc4W44zTlJaO9iOqv49LkXixmmWt4zmIXRJyTUlRNwESnhVZiQvMlIwyrLtEZpRLlgmeMGOU88SLCouTtFZCCtCSC3yeoamlzsYY_8KETcqQIvHfgj9YDusYlT6E7cQQcfBWTyF_XqwETqvYqK2ACNeg_J2D2wg9q51a9cNELBxHutdAx4H0JMf4g6D7ZXVsAEbz9GJUesAF791jj4e7t8XT9ny7fF5cbvMNKN1zCipC1PSGrSqKtaQvDUlqfO24DVLr4AwZasJU7xOU1MZUwggkDccBDNJxObo6uA7evc1QYhy5SZv00nJkhHlVJQisfIDS3sXggcjRz9slN9JSuQ-XnmIV6Z45U-8cptE7CAKiWw78H_W_6i-AUl4gKg</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Yamarthy, Anil Kumar</creator><creator>Koteswararao, Ch</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope></search><sort><creationdate>20240801</creationdate><title>MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement</title><author>Yamarthy, Anil Kumar ; Koteswararao, Ch</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-1094f619eca883b02df6092d4593857e7f6dc03a59857b8ff47e0e2b5e73feca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Classification</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Confidentiality</topic><topic>Cybercrime</topic><topic>Cybersecurity</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Digitization</topic><topic>Electronic mail systems</topic><topic>Fraud</topic><topic>Internet</topic><topic>Machine learning</topic><topic>Malware</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Operating Systems</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Performance evaluation</topic><topic>Personal information</topic><topic>Phishing</topic><topic>Processor Architectures</topic><topic>Websites</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yamarthy, Anil Kumar</creatorcontrib><creatorcontrib>Koteswararao, Ch</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Cluster computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yamarthy, Anil Kumar</au><au>Koteswararao, Ch</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement</atitle><jtitle>Cluster computing</jtitle><stitle>Cluster Comput</stitle><date>2024-08-01</date><risdate>2024</risdate><volume>27</volume><issue>5</issue><spage>6377</spage><epage>6395</epage><pages>6377-6395</pages><issn>1386-7857</issn><eissn>1573-7543</eissn><abstract>Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is phished or non-phished. Some anti-phishing software is used to identify the phishing data. However, few of these techniques did not provide efficient performance. Hence, the proposed model is introduced to overcome the issues obtained and improve the efficiency of detecting whether the data is phished or non-phished. The data is gathered from the Phishstorm dataset, which is pre-processed using the Z score normalization method and data cleaning. Data balancing is done by the Advanced synthetic sampling approach (Adv-SyN) to balance the dataset, and the features are extracted using a Double self-sparse autoencoder (DSelSa). The Opposition Gazelle optimization algorithm (OpGoA) model is used for optimal feature selection, and finally, the data is classified using Multi Head Depth wise Tern integrated long short term memory (MDepthNet). The sooty tern optimization is used to evaluate the loss function of the network model. The performance of the proposed model is analyzed based on some evaluation metrics and compared with other models, which describes the efficiency of the proposed model. The main objective of proposed technique used to detect the phishing attack and phishing or non-phishing. An automated DL methodology introduced for effective detection of phishing attacks for enhancing the cyber security. The accuracy of the proposed model is obtained as 99.45%, and Precision is 99.45%. RMSE and MSE rate of the proposed model is reduced to 0.73 and 0.05 for better performance.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10586-024-04313-w</doi><tpages>19</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1386-7857 |
ispartof | Cluster computing, 2024-08, Vol.27 (5), p.6377-6395 |
issn | 1386-7857 1573-7543 |
language | eng |
recordid | cdi_proquest_journals_3092151767 |
source | SpringerLink Journals - AutoHoldings |
subjects | Accuracy Algorithms Classification Computer Communication Networks Computer Science Confidentiality Cybercrime Cybersecurity Datasets Deep learning Digitization Electronic mail systems Fraud Internet Machine learning Malware Methods Neural networks Operating Systems Optimization Optimization algorithms Performance evaluation Personal information Phishing Processor Architectures Websites |
title | MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T12%3A23%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=MDepthNet%20based%20phishing%20attack%20detection%20using%20integrated%20deep%20learning%20methodologies%20for%20cyber%20security%20enhancement&rft.jtitle=Cluster%20computing&rft.au=Yamarthy,%20Anil%20Kumar&rft.date=2024-08-01&rft.volume=27&rft.issue=5&rft.spage=6377&rft.epage=6395&rft.pages=6377-6395&rft.issn=1386-7857&rft.eissn=1573-7543&rft_id=info:doi/10.1007/s10586-024-04313-w&rft_dat=%3Cproquest_cross%3E3092151767%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3092151767&rft_id=info:pmid/&rfr_iscdi=true |