• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.139-146
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• 2008

Despite of information security installation, leakage of personal information in web services has not decreased. This is because traffics to web applications are still vulnerable by permitting external sources to access services in port HTTF 80 and HTTPS 443, even with firewall systems in place. This thesis analyzes various attack patterns resulted from web service environment and vulnerable traffic and categorizes the traffics into normal and abnormal traffics. Also this proposes ways to analyze web application attack patterns from those abnormal traffics based on weak points warned in OWASF(Open Web Application Security Project), design a system capable of detect and isolate attacks in real time, and increase efficiency of preventing attacks.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.466-469
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• 2018

최근 PC를 비롯한 모바일, IOT 기기 등 다양한 환경에서의 사이버 공격이 기승을 부리고 있으며, 그 방법 또한 나날이 발전하고 있다. 이러한 사이버위협으로부터 개인 및 기업의 자산을 지키기 위한 근본적인 대안이 없이는 매번 반복적인 피해를 피하기 어려운 현실이다. 다양한 환경이라고 함은, 다양한 OS(Operation System), 다양한 ISA (Instruction Set Architecture)의 조합으로 이루어지는 사이버환경을 의미한다. 이러한 조합들은 일반 사용자들에게 가장 많이 쓰이는 Windows & Intel 조합의 환경과, Linux & Intel 또는 Linux & ARM 등 기업에서 서비스를 위해 쓰이는 서버 환경 등을 예로 들 수 있다. 그밖에 최근 IOT기기나 모바일 기기와 같은 환경도 있을 수 있다. 바이너리 파일에 대한 보안은 다양한 연구가 진행되고 있지만 그 범위가 방대하고, 깊이가 필요한 영역이라 진입 장벽이 높은 실정이다. 본 논문에서는 이러한 바이너리의 취약점을 분석하기 위한 첫 번째 단계로써 다양한 바이너리 파일을 하나의 정형화된 자료구조로 변환하는 바이너리 포맷 분석기의 한 방법을 제시하고자 한다. 다양한 OS와 다양한 ISA환경에서 사용되는 바이너리들에서 공통적으로 존재하는 정보들 중, 바이너리의 취약점 분석을 위해 필요한 데이터를 보다 효율적으로 수집하고, 관리하는 것이 바이너리를 통한 사이버 위협을 탐지하는 연구에서 기초가 된다고 할 수 있기 때문이다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.973-983
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• 2019

The importance of information security has been increasingly emphasized at the national, organizational, and individual levels due to the widespread adoption of software applications. High-safety software, which includes embedded software, should run without errors, similar to software used in the airline and nuclear energy sectors. Software development techniques in the above sectors are now being used to improve software security in other fields. Secure coding, in particular, is a concept encompassing defensive programming and is capable of improving software security. In this paper, we propose a software security vulnerability detection method using an improved coding standard searching technique. Public static analysis tools were used to assess software security and to classify the commands that induce vulnerability. Software security can be enhanced by detecting Application Programming Interfaces (APIs) and patterns that can induce vulnerability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.375-378
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• 2009

DDoS attacks make people can't using normal internet service, because DDoS attacks cause exhaustion of network bandwidth or exhaustion of computer system resources by using many personal computers or servers which already infected computer virus from hackers. Recent DDoS attacks attack government brach, financial institution, even IT security company. IT security companies make Anti-DDoS product for defense from DDoS attack. But, There is no standard for BMT of Anti-DDoS product. In this dissertation, Anti-DDoS product quality characteristics of the survey analysis to evaluate them by comparing the assessment items were derived.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.525-530
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• 2023

최근 소셜 네트워크 서비스(SNS) 및 모바일 서비스가 증가함에 따라 사용자들은 다양한 종류의 위험에 직면하고 있다. 특히 온라인 그루밍과 온라인 루머 같은 위험은 한 개인의 삶을 완전히 망가뜨릴 수 있을 정도로 심각한 문제로 자리 잡았다. 그러나 많은 경우 이러한 위험들을 판단하는 시점은 사건이 일어난 이후이고, 주로 법적인 증거채택을 위한 위험성 판별이 대다수이다. 따라서 본 논문은 이러한 문제를 사전에 예방하는 것에 초점을 맞추었고, 계속적으로 발생하는 대화와 같은 event를 실시간으로 감지하고, 위험을 사전에 탐지할 수 있는 Real-Time Early Risk Detection(RERD) 문제를 정의하고자 한다. 온라인 그루밍과 루머를 실시간 조기 위험 감지(RERD) 문제로 정의하고 해당 데이터셋과 평가지표를 소개한다. 또한 RERD 문제를 정확하고 신속하게 해결할 수 있는 강화학습 기반 새로운 방법론인 RT-ERD 모델을 소개한다. 해당 방법론은 RERD 문제를 이루고 있는 온라인 그루밍, 루머 도메인에 대한 실험에서 각각 기존의 모델들을 뛰어넘는 state-of-the-art의 성능을 달성하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.107-115
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• 2012

The level of information protection is relatively low, in comparison with the informatisation in this country. The budget for information protection is also quite marginal at 5% of the entire information-related policy budget. The passive information protection practices by companies, which focus more on the aftermaths, lead to repeated expenses for risk management. The responses to the violation of information protection should be changed from the current aftermaths-oriented focus to prevention and early detection of possible violations. We should also realize that the response to a violation of protected information is not a responsibility of an individual but a joint responsibility of the nation and the industry. South Korea has been working towards to building a systematic foundation since 2004 when guidelines were announced regarding the information protection policy and the safety diagnosis. The current level of safety policies cannot provide a perfect protection against actual violation cases in administrative, technological and physical ways. This research evaluates the level of prevention that the current systematic protection policy offers, and discusses its limitation and possible ways for improvement. It also recommends a list effective measures for protection against information violation that companies can employ to maintain the actual target safety level.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.1-8
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• 2009

Learning user's preference is a key issue in intelligent system such as personalized service. The study on user preference model has adapted simple user preference model, which determines a set of preferred keywords or topic, and weights to each target. In this paper, we recommend multi-perspective user preference model that factors sentiment information in the model. Based on the topicality and sentimental information processed using natural language processing techniques, it learns a user's preference. To handle timc-variant nature of user preference, user preference is calculated by session, short-term and long term. User evaluation is used to validate the effect of user preference teaming and it shows 86.52%, 86.28%, 87.22% of accuracy for topic interest, keyword interest, and keyword favorableness.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1235-1241
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• 2016

Personalized App recommendation system is recently famous since the number of various apps that can be used in smart phones that increases exponentially. However, the site users using google play site with malwares have experienced severe damages of privacy exposure and extortion as well as a simple damage of satisfaction descent at the same time. In addition, Sybil attack (Sybil) manipulating the score (rating) of each app with falmay also present because of the social networks development. Up until now, the sybil detection studies and malicious apps studies have been conducted independently. But it is important to determine finally the existence of intelligent attack with Sybil and malware simultaneously when we consider the intelligent attack types in real-time. Therefore, in this paper we experimentally evaluate the relationship between malware and sybils based on real cralwed dataset of goodlplay. Through the extensive evaluations, the correlation between malware and sybils is low for malware providers to hide themselves from Anti-Virus (AV).

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.315-324
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• 2011

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.