Phishing URL Detection with Lexical Features and Blacklisted Domains

Hong, Jiwon; Kim, Taeri; Liu, Jing; Park, Noseong; Kim, Sang–Wook

doi:10.1007/978-3-030-33432-1_12

Cited by 41 publications

(22 citation statements)

References 25 publications

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“…Authors maintained similar parameters for all detectors. However, the proposed method, LURL produced a better outcome rather than Hung Le et al [ 5 ] and Hong J. et al [ 6 ]. LURL covered 94.3 percent of data with learning rate of 5.0 whereas Hung Le et al and Hong J. et al have reached 93.8 and 92.8, respectively.…”

Section: Resultsmentioning

confidence: 71%

“…Social engineering attacks include Spear phishing, Whaling, SMS, Vishing, and mobile applications. In these attacks, attackers focus on the group of people or an organization and trick them to use the phishing URL [ 6 , 7 ]. Apart from these attacks, many new attacks are emerging exponentially as the technology evolves constantly.…”

Section: Research Background and Related Workmentioning

confidence: 99%

“…Authors in [ 6 ] introduced a method for phishing URLs with innovative lexical features and blacklist. They collected a list of URLs using a crawler from URL repositories and collected 18 common lexical features.…”

Section: Research Background and Related Workmentioning

confidence: 99%

“…Based on the related work and its performance, authors selected a couple of studies for comparing with the proposed URL detector. The studies of Hung Le et al, [ 5 ] and Hong J. et al, [ 6 ] were selected. The reason for selecting studies is that the studies were applied deep learning methods and achieved an average accuracy of 90%.…”

Section: Research Background and Related Workmentioning

confidence: 99%

“…The design and structure of HTML allow copying of images or an entire website [ 5 ]. Also, it is one of the factors for the rapid growth of Internet as a communication medium, and enables the misuse of brands, trademarks and other company identifiers that customers rely on as authentication mechanisms [ 6 – 8 ]. To trap users, Phisher sends "spooled" mails to as many people as possible.…”

Section: Introductionmentioning

confidence: 99%

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Detecting phishing websites using machine learning technique

Dutta

2021

PLoS ONE

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In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which consumers make online purchases and transactions. This growth leads to unauthorized access to users’ sensitive information and damages the resources of an enterprise. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested. However, due to inefficient security technologies, there is an exponential increase in the number of victims. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches. A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively. The experiments’ outcome shows that the proposed method’s performance is better than the recent approaches in malicious URL detection.

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Section: Resultsmentioning

confidence: 71%

Section: Research Background and Related Workmentioning

confidence: 99%

Section: Research Background and Related Workmentioning

confidence: 99%

Section: Research Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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