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

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.277-283
• /
• 2022

Email phishing has become very prevalent especially now that most of our dealings have become technical. The victim receives a message that looks as if it was sent from a known party and the attack is carried out through a fake cookie that includes a phishing program or through links connected to fake websites, in both cases the goal is to install malicious software on the user's device or direct him to a fake website. Today it is difficult to deploy robust cybersecurity solutions without relying heavily on machine learning algorithms. This research seeks to detect phishing emails using high-accuracy machine learning techniques. using the WEKA tool with data preprocessing we create a proposed methodology to detect emails phishing. outperformed random forest algorithm on Naïve Bayes algorithms by accuracy of 99.03 %.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.5
• /
• pp.165-170
• /
• 2023

With the recent rise of blockchain technology, cryptocurrency platforms using it are increasing, and currency transactions are being actively conducted. However, crimes that abuse the characteristics of cryptocurrency are also increasing, which is a problem. In particular, phishing scams account for more than a majority of Ethereum cybercrime and are considered a major security threat. Therefore, effective phishing scams detection methods are urgently needed. However, it is difficult to provide sufficient data for supervised learning due to the problem of data imbalance caused by the lack of phishing addresses labeled in the Ethereum participating account address. To address this, this paper proposes a phishing scams detection method that uses both Trans2vec, an effective graph embedding techique considering Ethereum transaction networks, and semi-supervised learning model Tri-training to make the most of not only labeled data but also unlabeled data.

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

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

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.3
• /
• pp.138-142
• /
• 2009

We propose an effective voice-phishing detection algorithm based on discriminative weight training. The detection of voice phishing is performed based on a Gaussian mixture model (GMM) incorporaiting minimum classification error (MCE) technique. Actually, the MCE technique is based on log-likelihood from the decoding parameter of the SMV(Selectable Mode Vocoder) directly extracted from the decoding process in the mobile phone. According to the experimental result, the proposed approach is found to be effective for the voice phishing detection.

• Journal of Service Research and Studies
• /
• v.5 no.2
• /
• pp.111-118
• /
• 2015

Although the development of mobile devices are made us a free life, they were displayed the subject of this financial crime and attacking forces in the other side. Among finance-related crime is become a serious crime that are targeting smartphones by SMS phishing, phishing, pharming, voice phishing etc. In particular, SMS phishing is increased according to phenomenon using the nature of a text message in the mobile. SMS phishing is become new crime due to the burden to the smartphone user. Their crime is also the advanced way from the existing fraud, such as making the malicious apps. Especially it generates loopholes in the law by a method such as using a foreign server. For safe from SMS phishing attacks, proactive pre-diagnosis is even more important rather than post responses. It is necessary to deploy blocking programs for detecting SMS phishing attacks in advance to do this. In this paper we are investigating the process of block types and block apps that are currently deployed and presenting the evaluation of the application of the detection block setting app.

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

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.10
• /
• pp.437-446
• /
• 2022

Text classification task from Natural Language Processing (NLP) combined with state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms as the core engine is widely used to detect and classify voice phishing call transcripts. While numerous studies on the classification of voice phishing call transcripts are being conducted and demonstrated good performances, with the increase of non-face-to-face financial transactions, there is still the need for improvement using the latest NLP technologies. This paper conducts a benchmarking of Korean voice phishing detection performances of the pre-trained Korean language model KoBERT, against multiple other SOTA algorithms based on the classification of related transcripts from the labeled Korean voice phishing dataset called KorCCVi. The results of the experiments reveal that the classification accuracy on a test set of the KoBERT model outperforms the performances of all other models with an accuracy score of 99.60%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.3
• /
• pp.1348-1375
• /
• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.