Cite this DOI

Mrs. Bessy, M. B. (2023). ENHANCED MALICIOUS URL DETECTION SYSTEM WITH MACHINE LEARNING ALGORITHMS. *Journal of Science & Technology*, *8*(7), 39–44. https://doi.org/10.46243/jst.2023.v8.i06.pp39-44

@article{mrsbessy2023enhanced,
  author    = {Mrs. Bessy, Mrs. Bessy},
  title     = {{ENHANCED MALICIOUS URL DETECTION SYSTEM WITH MACHINE LEARNING ALGORITHMS}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {aug},
  volume    = {8},
  number    = {7},
  pages     = {39--44},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i06.pp39-44},
  url       = {https://doi.org/10.46243/jst.2023.v8.i06.pp39-44},
  language  = {en},
  abstract  = {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.}
}

TY  - JOUR
TI  - ENHANCED MALICIOUS URL DETECTION SYSTEM WITH MACHINE LEARNING ALGORITHMS
AU  - Mrs. Bessy, Mrs. Bessy
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/08/07/
VL  - 8
IS  - 7
SP  - 39
EP  - 44
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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.
DO  - 10.46243/jst.2023.v8.i06.pp39-44
UR  - https://doi.org/10.46243/jst.2023.v8.i06.pp39-44
ER  -

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    "URL": "https://doi.org/10.46243/jst.2023.v8.i06.pp39-44",
    "title": "ENHANCED MALICIOUS URL DETECTION SYSTEM WITH MACHINE LEARNING ALGORITHMS",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
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    "volume": "8",
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    "page": "39-44",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "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.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i06.pp39-44 gives all four in one JSON answer.