• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.277-283
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• 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 %.

• 정보보호학회논문지
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• 제23권6호
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• pp.1037-1047
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• 2013

피싱(phishing) 공격의 피해가 증가함에 따라 그에 대한 대응책 및 관련 연구가 활발히 진행되어 왔다. 피싱 공격을 막는 솔루션(anti-phishing solutions)들은 웹브라우져(web-browser)의 보안 기능으로 혹은 보안 툴바(toolbar)의 형태로 다양하게 개발되었고, 그밖에도 이메일 필터링(email-filtering), 비밀 이미지 공유를 통한 서버 인증 강화 등의 방식으로 솔루션들이 개발되었다. 피싱 관련 연구로는 피싱 공격이 성공하는 이유에 대한 분석 연구, 피싱 공격을 막는 솔루션들이 효과적인지에 대해 사용자 입장에서 분석한 연구들이 발표되었다. 이 논문에서는 피싱 공격을 막는 다양한 종류의 솔루션들을 소개하고, 대표적인 솔루션들의 기술적 원리를 이해한다. 또한, 피싱 관련 연구 결과들을 함께 짚어봄으로써 피싱 공격을 막는 솔루션들이 개선되어야할 점들을 강조하여 살펴보고, 앞으로 피싱을 막기 위한 대책 연구가 진행되어야할 방향을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1348-1375
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• 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.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.284-287
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• 2013

최근 기업과 정부기관을 대상으로 한 목표지향적 공격이 증가하는 추세이다. 목표 지향적 공격이라 불리는 지능형 지속 위협(Advanced Persistent Threat) 공격의 시작은 공격 목표 조직에 근무하는 특정 대상자에게 사전 수집된 정보를 기반으로 철저히 개인화된 이메일, 즉 스피어 피싱 이메일을 보내는 것이다. 목표지향적 공격의 약 91%가 스피어 피싱 이메일을 사용하는 것으로 알려졌다. 본 논문에서는 최근 스피어 피싱 개요와 특징에 대해 설명하고 기존에 제안된 스피어 피싱 대응방안을 분석하였다. 이러한 분석과 이해를 바탕으로 최종 사용자 관점에서 효과적인 스피어 피싱 대응방안을 제안하였다.

• 한국정보통신학회논문지
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• 제17권12호
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• pp.2753-2762
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• 2013

최근 정보보호 기술의 발전으로 사이버 공격의 패러다임이 변화하고 있다. 조직의 시스템이나 네트워크에 대한 보호 대책이 강화되면서 최종 사용자를 대상으로 사회공학적 기법을 이용하는 사이버 공격이 증가하고 있다. 기업이나 정부기관을 대상으로 중요한 기밀 데이터를 절취하거나 중요 서비스를 무력화하는 지능형지속위협(APT, Advanced Persistent Threat) 공격의 약 91%가 스피어 피싱 이메일을 사용하는 것으로 알려졌다. 본 논문에서는 스피어 피싱 공격의 특성과 보안 위협을 자세히 분석하여 기술적인 통제항목만으로 스피어 피싱 공격에 대응하는 것에 한계가 있음을 설명하고 이를 보완할 수 있는 스피어 피싱 공격에 대한 관리적 대응방안을 제안하였다.

• 정보보호학회논문지
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• 제27권2호
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• pp.267-279
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• 2017

본 연구는 한 회사에서 실제 업무 중에 있는 임직원들을 대상으로 장기간 여러 차수에 걸쳐 외부 해커들이 공격하는 동일한 경로와 방식으로 피싱(phishing)메일을 발송하고, 차수가 경과됨에 따라 메일 수신자들의 피싱 메일에 대한 식별능력과 대응행동을 측정하였으며, 훈련 간 부가적으로 외부통제 조건을 변화시켜 수신자들의 대응행동이 추가적으로 어떻게 변화되는지를 분석하였다. 분석결과 단발적 훈련보다는 지속적인 훈련이 임직원들의 피싱메일 식별능력과 감염율 감소에 정(+)의 영향을 주고 있음을 확인하였으며, 사회적 이슈나 시기적 이벤트와 연계한 피싱공격에 더 많은 임직원들이 감염이 되며, 감염자에 대한 인사조치와 같은 내부통제정책 강화가 임직원들의 피싱공격 대응행동에 정(+)의 영향을 주고 있음을 확인할 수 있었다. 이러한 결과에 따라 각 기관이 임직원들의 피싱공격 대응역량 강화를 위한 올바른 훈련방향을 제시하고자 한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.297-299
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• 2021

Text classification is one of the popular tasks in Natural Language Processing (NLP) used to classify text or document applications such as sentiment analysis and email filtering. Nowadays, state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms are the core engine used to perform these classification tasks with high accuracy, and they show satisfying results. This paper conducts a benchmarking performance's analysis of multiple SOTA algorithms on the first known labeled Korean voice phishing dataset called KorCCVi. Experimental results reveal performed on a test set of 366 samples reveal which algorithm performs the best considering the training time and metrics such as accuracy and F1 score.

• 디지털산업정보학회논문지
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• 제4권3호
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• pp.45-53
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• 2008

As internet is wide spread, the number of internet service provider is increased. Internet service providers gather the personnel information with inhabitants registration identification number for the user management and the adult authentication. The personnel information is spreaded thorough the Internet by the system hacking, mismanagement and malicious resale. And the personnel information is used for spam email, phishing scams, etc. by malicious others. So the Ministry of Information and Communication Republic of Korea developments I-PIN system of the personnel identification. But, I-PIN has some problem the guideline for it and the method of 5 I-PIN services. In this paper, we analyze the problem about the guideline for I-PIN and the method of 5 I-PIN services. And we propose the countermeasure about the problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.