Machine learning approach for phishing website detection : A literature survey
The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at...
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| Vydané v: | Journal of discrete mathematical sciences & cryptography Ročník 25; číslo 3; s. 817 - 827 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Taylor & Francis
03.04.2022
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| ISSN: | 0972-0529, 2169-0065 |
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| Abstract | The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at the hands of internet miscreants. A phishing attack comprises an attacker that creates fake websites to fool users and steal client-sensitive data which may be in form of login, password, or credit card details. Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection. This thesis will discuss in detail, different approaches used by various authors over the past few years. This survey aims to identify and narrow down the best machine learning algorithms that can be adopted to develop a hybrid model which can be implemented to detect whether a website is legitimate or phishing in nature. |
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| AbstractList | The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at the hands of internet miscreants. A phishing attack comprises an attacker that creates fake websites to fool users and steal client-sensitive data which may be in form of login, password, or credit card details. Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection. This thesis will discuss in detail, different approaches used by various authors over the past few years. This survey aims to identify and narrow down the best machine learning algorithms that can be adopted to develop a hybrid model which can be implemented to detect whether a website is legitimate or phishing in nature. |
| Author | Patil, Rutuja R. Kaur, Gagandeep Tiwari, Ayush Rao, Keshav Sharma, Amit Jain, Himank Joshi, Soham |
| Author_xml | – sequence: 1 givenname: Rutuja R. surname: Patil fullname: Patil, Rutuja R. organization: Symbiosis Institute of Technology – sequence: 2 givenname: Gagandeep surname: Kaur fullname: Kaur, Gagandeep organization: Symbiosis Institute of Technology – sequence: 3 givenname: Himank surname: Jain fullname: Jain, Himank organization: Symbiosis Institute of Technology – sequence: 4 givenname: Ayush surname: Tiwari fullname: Tiwari, Ayush organization: Symbiosis Institute of Technology – sequence: 5 givenname: Soham surname: Joshi fullname: Joshi, Soham organization: Symbiosis Institute of Technology – sequence: 6 givenname: Keshav surname: Rao fullname: Rao, Keshav organization: Symbiosis Institute of Technology – sequence: 7 givenname: Amit surname: Sharma fullname: Sharma, Amit email: amit.25076@lpu.co.in organization: Lovely Professional University |
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| Cites_doi | 10.1016/j.procs.2020.03.294 10.1109/ICWR.2019.8765265 10.5120/ijca2018918026 10.1109/ACCESS.2020.2991403 10.1109/ICSTCEE49637.2020.9277256 10.1108/EL-05-2019-0118 10.1016/j.eswa.2018.09.029 10.1109/INMIC50486.2020.9318210 10.1109/ICCAIS48893.2020.9096869 10.35940/ijrte.B1018.0982S1119 10.1080/09720529.2020.1721877 10.1109/Confluence51648.2021.9377113 10.1080/09720529.2018.1526408 10.1109/ICAIS50930.2021.9395810 |
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| SubjectTerms | 68P27(Privacy of data) 68T05 (Learning and adaptive systems in artificial intelligence) Covid-19 Cyber attack Machine learning Phishing detection Web security |
| Title | Machine learning approach for phishing website detection : A literature survey |
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