• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.146-147
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• 2008

• Review of KIISC
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• v.21 no.2
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• pp.47-52
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• 2011

본 고는 ITU-T SG17 Q.5 "Countering spam by technical means"에서의 스팸 대응 기술 표준화 현황을 정리한다. Q.5/17에서의 스팸차단기술 표준화는 주로 이메일 스팸, IP 멀티미디어 스팸, 모바일 단문메시지서비스 스팸을 대상으로하며, 스팸 차단을 위한 상위 수준의 개요 표준, 기술 프레임워크 표준 및 기술 기법에 대한 표준 개발이 이루어지고 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.487-489
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• 2004

스팸 메일의 정의와 피해 현황을 살펴보고, 스팸 메일의 제도적ㆍ기술적인 규제 방법을 알아본다. 기술적인 규제 방법은 메일 클라이언트/서버 차원의 스팸 방지 방법, ASRG에서 제안한 메일 프로토콜과 헤더를 이용한 스팸 규제 방법, 온라인 우표제를 통한 스팸 규제 방법이 있다. 살펴본 규제 방법들을 분류해보고, 결론과 향후 연구 과제를 제시한다.

• Proceedings of the Zoological Society Korea Conference
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• 1999.10b
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• pp.38-38
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• 1999

No Abstract, See Full Text

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2301-2308
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• 2013

These days, Rehabilitation psychology analysis system is being used by world wide web in everyday's life. And on the other hand, we are facing spam messages' problems. To block these spam message, we are using filtering or pricing systems. But these solutions are raising other problems such as impediment in reception or availability caused by false positive or payment resistance. To solve these problems, we propose an Indirect Model on Message Control System(IMMCS) which controls an unsolicited message and prevents an useful message from discarding. We design and implement the IMMCS to enhance the usefulness and the availability. Being tested the IMMCS to verify the usability and the efficiency, it gave us a very successful result.

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.95-99
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• 2014

본 논문에서는 최근 급속히 증가하여 사회적 이슈가 되고 있는 SMS 스팸 필터링을 위한 듀얼 SMS 스팸필터링 기법을 제안한다. 지속적으로 증가하고 새롭게 변형되는 SMS 문자 필터링을 위해서는 패턴 및 스팸 단어 사전을 통한 필터링은 많은 수작업을 요구하여 부적합하다. 그리하여 기계 학습을 이용한 자동화 시스템 구축이 요구되고 있으며, 효과적인 기계 학습을 위해서는 자질 선택과 자질의 가중치 책정 방법이 중요하다. 하지만 SMS 문자 특성상 문장들이 짧기 때문에 출현하는 자질의 수가 적어 분류의 어려움을 겪게 된다. 이 같은 문제를 개선하기 위하여 본 논문에서는 슬라이딩 윈도우 기반 N-gram 확장을 통해 자질을 확장하고, 확장된 자질로 그래프를 구축하여 얕은 구조적 특징을 표현한다. 학습 데이터에 출현한 N-gram 자질을 정점(Vertex)으로, 자질의 출현 빈도를 그래프의 간선(Edge)의 가중치로 설정하여 햄(HAM)과 스팸(SPAM) 그래프를 각각 구성한다. 이렇게 구성된 그래프를 바탕으로 노드의 중요도와 간선의 가중치를 활용하여 최종적인 자질의 가중치를 결정한다. 입력 문자가 도착하면 스팸과 햄의 그래프를 각각 이용하여 입력 문자의 2개의 자질 벡터(Vector)를 생성한다. 생성된 자질 벡터를 지지 벡터 기계(Support Vector Machine)를 이용하여 각 SVM 확률 값(Probability Score)을 얻어 스팸 여부를 결정한다. 3가지의 실험환경에서 바이그램 자질과 이진 가중치를 사용한 기본 시스템보다 F1-Score의 약 최대 2.7%, 최소 0.5%까지 향상되었으며, 결과적으로 평균 약 1.35%의 성능 향상을 얻을 수 있었다.

• The Journal of Korean Association of Computer Education
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• v.10 no.3
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• pp.57-66
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• 2007

E-mail system is considered as a most important communication media, which can be used to transmit personal information by internet. But e-mail attack also has been increased by spoofing e-mail sender address. Therefore, this work proposes sender verification faculty for spam mail protection at sender's MTA by using security card for protection forged sender and also for authenticating legal sender. Sender's mail MT A requests security card's code number to sender. Then sender input code number and generate session key after sender verification. Session key is used to encrypt sender's signature and secure message transmission. This work can provide efficient and secure e-mail sender authentication with sender verification and message encryption.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.835-846
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• 2020

BEC (Business Email Compromise) attacks are frequently occurring by impersonating accounts or management through e-mail and stealing money or sensitive information. This type of attack accounts for the largest portion of the recent trade fraud, and the FBI estimates that the estimated amount of damage in 2019 is about $17 billion. However, if you look at the response status of the companies compared to this, it relies on the traditional SPAM blocking system, so it is virtually defenseless against the BEC attacks that social engineering predominates. To this end, we will analyze the types and methods of BEC accidents and propose ways to effectively counter BEC attacks by companies through AI(Artificial Intelligence).

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.288-294
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• 2021

There are various ways to be infected with malicious code due to the increase in Internet use, such as the web, affiliate programs, P2P, illegal software, DNS alteration of routers, word processor vulnerabilities, spam mail, and storage media. In addition, malicious codes are produced more easily than before through automatic generation programs due to evasion technology according to the advancement of production technology. In the past, the propagation speed of malicious code was slow, the infection route was limited, and the propagation technology had a simple structure, so there was enough time to study countermeasures. However, current malicious codes have become very intelligent by absorbing technologies such as concealment technology and self-transformation, causing problems such as distributed denial of service attacks (DDoS), spam sending and personal information theft. The existing malware detection technique, which is a signature detection technique, cannot respond when it encounters a malicious code whose attack pattern has been changed or a new type of malicious code. In addition, it is difficult to perform static analysis on malicious code to which code obfuscation, encryption, and packing techniques are applied to make malicious code analysis difficult. Therefore, in this paper, a method to detect malicious code through dynamic analysis and static analysis using Trojan-type Downloader/Dropper malicious code was showed, and suggested to malicious code detection and countermeasures.