Phishing Detection: Analysis of Visual Similarity Based Approaches

Jain, Ankit Kumar; Gupta, Brij B.

doi:10.1155/2017/5421046

Cited by 147 publications

(70 citation statements)

References 63 publications

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“…The term "phishing" was first used in 1996, when hackers stole users" confidential information from America On-line (AOL) [1], [2]. In that incident, the hackers contacted AOL users via fake emails and asked them to verify their passwords for security purposes.…”

Section: Related Workmentioning

confidence: 99%

“…One such threat is posed by phishing, whereby a criminal (phisher) will attempt to steal a user"s sensitive information (such as credit and debit card details, bank account details, and address) using fake emails, fake websites, or both [1]. Therefore, phishing attacks have become one of the most serious types of threat to businesses and the public in recent years [2]. According to the Canadian Center for Cyber Security [3], the number of reported phishing attacks is around 156 million emails per day, and the number of victims reaches around 80,000 per day.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in 2005, the Bank of America lost 1.2 million usernames and social security numbers (SSNs) belonging to its customers, which led to the loss of millions of dollars. In 2011, the details of 10 million credit cards belonging to users were stolen from Sony Entertainments, which cost approximately two billion dollars, making it the most expensive cyber-hack in history [2]. The FBI"s Internet Crime Report for 2017 counts phishing attacks amongst the top three types of crime cited by victims of Internet crime, with losses of approximately 30 million dollars being recorded for that year.…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation of User Awareness for the Detection of Phishing Emails

Alwanain¹

2019

IJACSA

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Phishing attacks are among the most serious Internet criminal activities. They aim to make Internet users believe that they are using a trusted entity, for the purpose of stealing sensitive information, such as bank account or credit card information. Phishing costs Internet users millions of dollars each year. An effective method that can prevent such attacks is improving the security awareness of Internet users, especially in light of the significant growth of online services. This paper discusses a real-world experiment, which aims to analyze and monitor the phishing awareness of an organization's users in order to improve their awareness. The experiments have been targeting 1500 users in the education sector. The results of the experiment reveal that phishing awareness has a significant positive effect on users' ability to distinguish phishing emails and websites, thereby avoiding attacks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Evaluation of User Awareness for the Detection of Phishing Emails

Alwanain¹

2019

IJACSA

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“…This result implies that the proposed system cannot detect the phishing website like this. As mentioned in a review paper [17], almost all of visual similarity-based phishing detection schemes are based on the fact that phishing websites look very similar in appearance to their corresponding legitimate websites to attract large number of Internet users. From this point of view, while it is difficult for most of visual similarity-based approaches to detect such phishing websites, the possibility that facebook's users are deceived by red colored facebook is low due to its strange looks.…”

Section: Phishing Website With Manipulated Colormentioning

confidence: 99%

Hue Signature Auto Update System for Visual Similarity-Based Phishing Detection with Tolerance to Zero-Day Attack

Haruta

Asahina

Yamazaki

et al. 2019

IEICE Trans. Inf. & Syst.

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Shuichiro HARUTA †a) , Hiromu ASAHINA †b) , Student Members, Fumitaka YAMAZAKI †c) , Nonmember, and Iwao SASASE †d) , Fellow SUMMARY Detecting phishing websites is imperative. Among several detection schemes, the promising ones are the visual similarity-based approaches. In those, targeted legitimate website's visual features referred to as signatures are stored in SDB (Signature Database) by the system administrator. They can only detect phishing websites whose signatures are highly similar to SDB's one. Thus, the system administrator has to register multiple signatures to detect various phishing websites and that cost is very high. This incurs the vulnerability of zero-day phishing attack. In order to address this issue, an auto signature update mechanism is needed. The naive way of auto updating SDB is expanding the scope of detection by adding detected phishing website's signature to SDB. However, the previous approaches are not suitable for auto updating since their similarity can be highly different among targeted legitimate website and subspecies of phishing website targeting that legitimate website. Furthermore, the previous signatures can be easily manipulated by attackers. In order to overcome the problems mentioned above, in this paper, we propose a hue signature auto update system for visual similarity-based phishing detection with tolerance to zero-day attack. The phishing websites targeting certain legitimate website tend to use the targeted website's theme color to deceive users. In other words, the users can easily distinguish phishing website if it has highly different hue information from targeted legitimate one (e.g. red colored Facebook is suspicious). Thus, the hue signature has a common feature among the targeted legitimate website and subspecies of phishing websites, and it is difficult for attackers to change it. Based on this notion, we argue that the hue signature fulfills the requirements about auto updating SDB and robustness for attackers' manipulating. This commonness can effectively expand the scope of detection when auto updating is applied to the hue signature. By the computer simulation with a real dataset, we demonstrate that our system achieves high detection performance compared with the previous scheme.

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“…An SMS spam message is an unwanted or unsolicited text message which is sent to the user's mobile phone with various content types, such as advertisements, awards, free services and promotions [4]. The main goal of spammers is to steal critical user information such as *Corresponding Author Email: s.sheikhi@outlook.com (S. Sheikhi) username, password, and credit card details [5]. They may impose various strategies in order to steal information, hence, SMS messages being one the most straightforward tactics [6].…”

Section: Introductionmentioning

confidence: 99%

An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network

Sheikhi

Kheirabadi

Bazzazi

2020

IJE

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In recent years, there has been considerable interest among people to use short message service (SMS) as one of the essential and straightforward communications services on mobile devices. The increased popularity of this service also increased the number of mobile devices attacks such as SMS spam messages. SMS spam messages constitute a real problem to mobile subscribers; this worries telecommunication service providers as it disturbs their customers and causes them to lose business. Therefore, in this paper, we proposed a novel machine learning method for detection of SMS spam messages. The proposed model contains two main stages: feature extraction and decision making. In the first stage, we have extracted relevant features from the dataset based on the characteristics of spam and legitimate messages to reduce the complexity and improve performance of the model. Then, an averaged neural network model was applied on extracted features to classify messages into either spam or legitimate classes. The method is evaluated in terms of accuracy and F-measure metrics on a realworld SMS dataset with over 5000 messages. Moreover, the achieved results were compared against three recently published works. Our results show that the proposed approach achieved successfully high detection rates in terms of F-measure and classification accuracy, compared with other considered researches.

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Phishing Detection: Analysis of Visual Similarity Based Approaches

Cited by 147 publications

References 63 publications

An Evaluation of User Awareness for the Detection of Phishing Emails

An Evaluation of User Awareness for the Detection of Phishing Emails

Hue Signature Auto Update System for Visual Similarity-Based Phishing Detection with Tolerance to Zero-Day Attack

An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network

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