• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.313-320
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• 2015

While various phishing attacks are getting to be increased in constant, their response methods still stay on the stage of responding after identifying an attack. To detect a phishing site ahead of an attack, a method has been suggested with utilizing the Referer header field of HTTP. However, it has a limitation to implement a traffic gathering system for each of prospective target hosts. This paper presents a unknown phishing site detection method in the Interior network environment. Whenever a user try to connect a phishing site, its traffic is pre-processed with considering of the characteristics of HTTP protocol and phishing site. The phishing site detection phase detects a suspicious site under phishing with analysing HTTP content. To validate the proposed method, some evaluations were conducted with 100 phishing URLs along with 100 normal URLs. The experimental results show that our method achieves higher phishing site detection rate than that of existing detection methods, as 66% detection rate for the phishing URLs, and 0% false negative rate for the normal URLs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.819-825
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• 2012

Nowadays many phishing sites contain HTTP links to victim web-site's contents such as images, bulletin board etc. to make the phishing sites look more real and similar to the victim web-site. We introduce a real-time phishing site detection system which makes use of the characteristic that the phishing sites' URLs flow into the victim web-site via the HTTP referer header field when the phishing site is visited. The detection system is designed to adopt an out-of-path network configuration to minimize effect on the running system, and a phishing site source code analysis technique to alert administrators in real-time when phishing site is detected. The detection system was installed on a company's web-site which had been targeted for phishing. As result, the detection system detected 40 phishing sites in 6 days of test period.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.10
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• pp.349-360
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• 2015

In recent year, phishing attacks are flooding with services based on the web technology. Phishing is affecting online security significantly day by day with the vulnerability of web pages. To prevent phishing attacks, a lot of anti-phishing techniques has been made with their own advantages and dis-advantages respectively, but the phishing attack has not been eradicated completely yet. In this paper, we have studied phishing in detail and categorize a process of phishing attack in two parts - Landing-phase, Attack-phase. In addition, we propose an phishing detection methodology based on web sites heuristic. To extract web sites features, we focus on URL and source codes of web sites. To evaluate performance of the suggested method, set up an experiment and analyze its results. Our methodology indicates the detection accuracy of 98.9% with random forest algorithm. The evaluation of proof-of-concept reveals that web site features can be used for phishing detection.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.272-282
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• 2022

Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.431-434
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• 2007

피싱(Phishing)은 불특정 다수의 이메일 사용자에게 신용카드나 은행계좌정보에 문제가 발생해 수정이 필요하다는 거짓 이메일을 발송하여 관련 금융 기관의 신용카드 정보나 계좌정보를 등을 빼내는 해킹 기법으로써, 개인정보(Private data)와 낚시(Fishing)의 합성어로 낚시하듯이 개인정보를 몰래 빼내는 것을 말한다. 이 논문에서는 개인정보를 훔쳐가는 피싱의 유형과 방법을 분석하고 피싱(Phishing) 웹사이트를 탐지하는 방법을 제시 할 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.607-610
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• 2008

Phishing is a form of online identity theft that aims to steal sensitive information such as online banking passwords and credit card information from users. Phishing scams have been receiving extensive press coverage because such attacks have been escalating in number and sophistication. According to a study by Gartner, Many Internet users have identified the receipt of e-mail linked to phishing scams and about 2 million of them are estimated to have been tricked into giving away sensitive information. This paper presents a novel browser extension, AntiPhish, that aims to protect users against spoofed web site-based phishing attack.

• Convergence Security Journal
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• v.23 no.3
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• pp.29-36
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• 2023

With the recent surge in exposure to fake advertising phishing sites in search engines, the damage caused by poor search quality and personal information leakage is increasing. In particular, the seriousness of the problem is worsening faster as the possibility of automating the creation of advertising phishing sites through tools such as ChatGPT increases. In this paper, the source code of fake advertising phishing sites was statically analyzed to derive structural commonalities, and among them, a detection crawler that filters sites step by step based on foreign domains and redirection was developed to confirm that fake advertising posts were finally detected. In addition, we demonstrate the need for new guide lines by verifying that the redirection page of fake advertising sites is divided into three types and returns different sites according to each situation. Furthermore, we propose new detection guidelines for fake advertising phishing sites that cannot be detected by existing detection methods.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.79-90
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• 2014

User authentication based on ID and PW has been widely used. As the Internet has become a growing part of people' lives, input times of ID/PW have been increased for a variety of services. People have already learned enough to perform the authentication procedure and have entered ID/PW while ones are unconscious. This is referred to as the adaptive unconscious, a set of mental processes incoming information and producing judgements and behaviors without our conscious awareness and within a second. Most people have joined up for various websites with a small number of IDs/PWs, because they relied on their memory for managing IDs/PWs. Human memory decays with the passing of time and knowledges in human memory tend to interfere with each other. For that reason, there is the potential for people to enter an invalid ID/PW. Therefore, these characteristics above mentioned regarding of user authentication with ID/PW can lead to human vulnerabilities: people use a few PWs for various websites, manage IDs/PWs depending on their memory, and enter ID/PW unconsciously. Based on the vulnerability of human factors, a variety of information leakage attacks such as phishing and pharming attacks have been increasing exponentially. In the past, information leakage attacks exploited vulnerabilities of hardware, operating system, software and so on. However, most of current attacks tend to exploit the vulnerabilities of the human factors. These attacks based on the vulnerability of the human factor are called social-engineering attacks. Recently, malicious social-engineering technique such as phishing and pharming attacks is one of the biggest security problems. Phishing is an attack of attempting to obtain valuable information such as ID/PW and pharming is an attack intended to steal personal data by redirecting a website's traffic to a fraudulent copy of a legitimate website. Screens of fraudulent copies used for both phishing and pharming attacks are almost identical to those of legitimate websites, and even the pharming can include the deceptive URL address. Therefore, without the supports of prevention and detection techniques such as vaccines and reputation system, it is difficult for users to determine intuitively whether the site is the phishing and pharming sites or legitimate site. The previous researches in terms of phishing and pharming attacks have mainly studied on technical solutions. In this paper, we focus on human behaviour when users are confronted by phishing and pharming attacks without knowing them. We conducted an attack experiment in order to find out how many IDs/PWs are leaked from pharming and phishing attack. We firstly configured the experimental settings in the same condition of phishing and pharming attacks and build a phishing site for the experiment. We then recruited 64 voluntary participants and asked them to log in our experimental site. For each participant, we conducted a questionnaire survey with regard to the experiment. Through the attack experiment and survey, we observed whether their password are leaked out when logging in the experimental phishing site, and how many different passwords are leaked among the total number of passwords of each participant. Consequently, we found out that most participants unconsciously logged in the site and the ID/PW management dependent on human memory caused the leakage of multiple passwords. The user should actively utilize repudiation systems and the service provider with online site should support prevention techniques that the user can intuitively determined whether the site is phishing.

• Journal of IKEEE
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• v.27 no.3
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• pp.213-219
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• 2023

According to statistics from the National Police Agency, smishing crimes using texts or messengers have increased dramatically since COVID-19. In addition, most of the cases of impersonation of public institutions reported to agency were related to vaccination and reward, and many methods were used to trick people into clicking on fake URLs (Uniform Resource Locators). When detecting them, URL-based detection methods cannot detect them properly if the information of the URL is hidden, and content-based detection methods are slow and use a lot of resources. In this paper, we propose a system for URL-based detection using transformer for regular URLs and content-based detection using XGBoost for shortened URLs through the process of determining shortened URLs using GRU(Gated Recurrent Units). The F1-Score of the proposed detection system was 94.86, and its average processing time was 5.4 seconds.

• Convergence Security Journal
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• v.23 no.1
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• pp.19-25
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• 2023

The current network environment provides a real-time interactive service from an initial one-way information prov ision service. Depending on the form of web-based information sharing, it is possible to provide various knowledge a nd services between users. However, in this web-based real-time information sharing environment, cases of damage by illegal attackers who exploit network vulnerabilities are increasing rapidly. In particular, for attackers who attempt a phishing attack, a link to the corresponding web page is induced after actively generating a forged web page to a user who needs a specific web page service. In this paper, we analyze whether users directly and actively forge a sp ecific site rather than a passive server-based detection method. For this purpose, it is possible to prevent leakage of important personal information of general users by detecting a disguised webpage of an attacker who induces illegal webpage access using traceback information