A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security
| dc.authorid | Kabakuş, Abdullah Talha/0000-0003-2181-4292 | |
| dc.authorwosid | Kabakuş, Abdullah Talha/J-8361-2019 | |
| dc.contributor.author | Kabakuş, Abdullah Talha | |
| dc.date.accessioned | 2023-07-26T11:50:05Z | |
| dc.date.available | 2023-07-26T11:50:05Z | |
| dc.date.issued | 2023 | |
| dc.department | DÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | Uniform resource locator (URL)-based cyber-attacks form a major part of security threats in cyberspace. Even though the experience and awareness of the end-users help them protect themselves from these attacks, a software-based solution is necessary for comprehensive protection. To this end, a novel robust URL classification model based on convolutional neural network is proposed in this study. The proposed model classifies given URLs into five classes, namely, (i$$ \mathrm{i} $$) benign$$ \mathrm{benign} $$, (ii$$ \mathrm{ii} $$) defacement$$ \mathrm{defacement} $$, (iii$$ \mathrm{iii} $$) phishing$$ \mathrm{phishing} $$, (iv$$ \mathrm{iv} $$) spam$$ \mathrm{spam} $$, and (v$$ \mathrm{v} $$) malware$$ \mathrm{malware} $$. The proposed model was trained and evaluated on a gold standard URL dataset comprising of 36,707$$ \mathrm{36,707} $$ samples. According to the experimental result, the proposed model obtained an accuracy as high as 98.1%$$ 98.1\% $$ which outperformed the state-of-the-art. Based on the same architecture, we proposed another classifier, a binary classifier that detects malicious URLs without dealing with their types. This binary classifier obtained an accuracy as high as 99.3%$$ 99.3\% $$ which outperformed the state-of-the-art as well. The experimental result demonstrates the feasibility of the proposed solution. | en_US |
| dc.description.sponsorship | [ISCX-URL-2016] | en_US |
| dc.description.sponsorship | ACKNOWLEDGMENT We would like to thank the maintainers of the ISCX-URL-2016 for publicly sharing their dataset as a contribution to the research field. | en_US |
| dc.identifier.doi | 10.1002/cpe.7517 | |
| dc.identifier.issn | 1532-0626 | |
| dc.identifier.issn | 1532-0634 | |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.scopus | 2-s2.0-85143274254 | en_US |
| dc.identifier.scopusquality | Q3 | en_US |
| dc.identifier.uri | https://doi.org/10.1002/cpe.7517 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12684/12226 | |
| dc.identifier.volume | 35 | en_US |
| dc.identifier.wos | WOS:000888000300001 | en_US |
| dc.identifier.wosquality | Q3 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.institutionauthor | Kabakuş, Abdullah Talha | |
| dc.language.iso | en | en_US |
| dc.publisher | Wiley | en_US |
| dc.relation.ispartof | Concurrency and Computation-Practice & Experience | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.snmz | $2023V1Guncelleme$ | en_US |
| dc.subject | Convolutional Neural Network; Cyber Security; Deep Neural Network; Malicious Url Classification; Malicious Url Detection | en_US |
| dc.title | A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security | en_US |
| dc.type | Article | en_US |
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