Kernel Metadata Declaration — on demand

What the DOI identifies

ENHANCED MALICIOUS URL DETECTION SYSTEM WITH MACHINE LEARNING ALGORITHMS

10.46243/jst.2023.v8.i06.pp39-44 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2023-08-07

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 7 · pp. 39–44

Principal agents

• Mrs. Bessy Mrs. Bessy (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 3 links, 11 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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    "abstract": {
        "value": "Currently, the risk of network information insecurity is increasing rapidly in number and level of danger. The methods mostly used by hackers today is to attack end-to end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As a results, Malicious URL detection is of great interest nowadays. There have been several scientific studies showing several methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes. Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviors. In short, the proposed detection system consists of a new set of URLs features and behaviors, a machine learning algorithm, and a big data technology. The experimental results show that the proposed URL attributes and behavior can help improve the ability to detect malicious URL significantly. This is suggested that the proposed system may be considered as anoptimized and friendly used solution for malicious URL detection.",
        "lang": "en"
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    "license": {
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            "unstructured": "D. Sahoo, C. Liu, S.C.H. Hoi, “Malicious URL Detection using Machine Learning: A Survey”. CoRR, abs/1701.07179, 2017"
        },
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            "key": "ref2",
            "doi": "10.1109/surv.2013.032213.00009",
            "unstructured": "M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: a literature survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2091–2121, 2013"
        },
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            "key": "ref3",
            "doi": "10.1145/1772690.1772720",
            "unstructured": "M. Cova, C. Kruegel, and G. Vigna, “Detection and analysis of drivebydownload attacks and maliciousjavascript code,” in Proceedings of the 19th international conference on Worldwide web. ACM, 2010, pp. 281–290"
        },
        {
            "key": "ref4",
            "doi": "10.1145/2835375",
            "unstructured": "R. Heartfield and G. Loukas, “A taxonomy of attacks and a survey of defense mechanisms for semantic social engineering attacks,” ACM Computing Surveys (CSUR), vol. 48, no. 3, p. 37, 2015"
        },
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            "key": "ref5",
            "unstructured": "Internet Security Threat Report (ISTR) 2019–Symantec. https://www.symantec.com/ content/dam/symantec/docs/reports/istr-24- 2019- en.pdf [Last accessed 10/2019]"
        },
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            "key": "ref6",
            "unstructured": "S. Sheng, B. Wardman, G. Warner, L. F. Cranor, J. Hong, and C. Zhang, “An empirical analysis of phishing blacklists,” in Proceedings of Sixth Conference on Email and Anti-Spam (CEAS), 2009"
        },
        {
            "key": "ref7",
            "doi": "10.1109/atnac.2008.4783302",
            "unstructured": "C. Seifert, I. Welch, and P. Komisarczuk, “Identification of malicious web pages with static heuristics,” in Telecommunication Networks and Applications Conference, 2008. ATNAC 2008. Australasian. IEEE,2008, pp. 91–96"
        },
        {
            "key": "ref8",
            "doi": "10.1109/malware.2008.4690858",
            "unstructured": "S. Sinha, M. Bailey, and F. Jahanian, “Shades of grey: On the effectiveness of reputation-based “blacklists”,” in Malicious and Unwanted Software, 2008. MALWARE 2008. 3rd International Conference on. IEEE, 2008, pp. 57–64"
        },
        {
            "key": "ref9",
            "doi": "10.1145/1553374.1553462",
            "unstructured": "J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Identifying suspicious urls: an application of large-scale online learning,” in Proceedings of the 26th Annual International Conference onMachine Learning. ACM, 2009, pp. 681–688"
        },
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            "key": "ref10",
            "doi": "10.1007/978-3-642-36883-7_10",
            "unstructured": "B. Eshete, A. Villafiorita, and K. Weldemariam, “Binspect: Holistic analysis and detection of malicious web pages,” in Security and Privacy in Communication Networks. Springer, 2013, pp. 149–166"
        },
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            "key": "ref11",
            "doi": "10.1108/09685221211286548",
            "unstructured": "S. Purkait, “Phishing counter measures and their effectiveness–literature review,” Information Management & Computer Security, vol. 20, no. 5, pp. 382–420, 2012"
        }
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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