• The KIPS Transactions:PartC
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• v.13C no.4 s.107
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• pp.405-414
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• 2006

We present a symptom based taxonomy for network security. This taxonomy classifies attacks in the network using early symptoms of the attacks. Since we use the symptom it is relatively easy to access the information to classify the attack. Furthermore we are able to classify the unknown attack because the symptoms of unknown attacks are correlated with the one of known attacks. The taxonomy classifies the attack in two stages. In the first stage, the taxonomy identifies the attack in a single connection and then, combines the single connections into the aggregated connections to check if the attacks among single connections may create the distribute attack over the aggregated connections. Hence, it is possible to attain the high accuracy in identifying such complex attacks as DDoS, Worm and Bot We demonstrate the classification of the three major attacks in Internet using the proposed taxonomy.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.791-794
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• 2006

컴퓨터의 정상적인 활동을 방해하는 공격행위는 네트워크로 연결된 컴퓨터를 기반으로 하는 사회 활동이 증가함에 따라 심각한 문제를 유발하고 있다. 하지만 기존의 네트워크 및 시스템공격에 대한 분류기법은 주로 공격자 입장에서 연구되어서 피해를 입은 시스템이 사용하기에는 부족하였다. 그래서 피해시스템 입장에서 공격을 정확히 분류하고 탐지할 수 있는 분류기법을 개발하는 것은 중요하다. 본 논문에서는 기존의 공격 분류방식을 분석하여 문제점을 발견한 후, 공격 분류방식이 가져야할 요구사항을 도출한다. 공격 분류기법의 요구사항을 만족하면서, 피해 시스템의 관리자가 공격에 대한 대책수립에 도움이 되는 공격 분류기법을 제안한다. 제안하는 분류기법은 공격방식에 따라 확장이 가능하므로 복합적 공격을 보다 정확하게 분류할 수 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1227-1236
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• 2021

Adversarial attack, one of the attacks on deep learning classification model, is attack that add indistinguishable perturbations to input data and cause deep learning classification model to misclassify the input data. There are various adversarial attack algorithms. Accordingly, many studies have been conducted to detect adversarial attack but few studies have been conducted to classify what adversarial attack algorithms to generate adversarial input. if adversarial attacks can be classified, more robust deep learning classification model can be established by analyzing differences between attacks. In this paper, we proposed a model that detects and classifies adversarial attacks by constructing a random forest classification model with input features extracted from a target deep learning model. In feature extraction, feature is extracted from a output value of hidden layer based on class predicted by the target deep learning model. Through Experiments the model proposed has shown 3.02% accuracy on clean data, 0.80% accuracy on adversarial data higher than the result of pre-existing studies and classify new adversarial attack that was not classified in pre-existing studies.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1496-1499
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• 2008

DoS/DDoS로 대표되는 트래픽 폭주 공격은 대상 시스템뿐만 아니라 네트워크 대역폭 및 시스템 자원 등을 고갈시킴으로써 네트워크에 심각한 장애를 유발하기 때문에, 신속한 공격 탐지와 공격유형별 분류는 안정적인 서비스 제공 및 시스템 운영에 필수요건이다. 본 논문에서는 1) 데이터마이닝의 대표적인 분류 모델인 C4.5 알고리즘을 기반으로 SNMP MIB 정보를 사용하여 트래픽 폭주공격을 탐지하고 각 공격유형별 분류를 수행하는 시스템을 설계 및 구현하였다; 2) C4.5에서 추가적으로 제공하는 동작원리에 관한 규칙들을 상세히 분석함으로써 공격탐지 및 공격유형별 분류에 관한 시스템의 의미론적 해석을 시도하였다; 3) C4.5는 주어진 SNMP MIB의 속성들의 정보이익 값을 이용하여 예측모형을 구축하는 알고리즘으로, 특징선택 및 축소의 효과를 추가적으로 얻었다. 따라서 시스템의 운용 시, 제안된 모델은 전체 13개의 MIB 정보 중 5개의 MIB 정보만을 사용하여 보다 신속하고, 정확하며, 또한 가벼운 공격탐지 및 공격유형별 분류를 수행함으로써 네트워크 시스템의 자원관리와 효율적인 시스템 운영에 기여하였다.

• Convergence Security Journal
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• v.20 no.4
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• pp.81-90
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• 2020

In the real world, new types of attacks or variants are constantly emerging, but attack traffic classification models developed through artificial neural networks and supervised learning do not properly detect new types of attacks that have not been trained. Most of the previous studies overlooked this problem and focused only on improving the structure of their artificial neural networks. As a result, a number of new attacks were frequently classified as normal traffic, and attack traffic classification performance was severly degraded. On the other hand, the softmax function, which outputs the probability that each class is correctly classified in the multi-class classification as a result, also has a significant impact on the classification performance because it fails to calculate the softmax score properly for a new type of attack traffic that has not been trained. In this paper, based on this characteristic of softmax function, we propose an efficient method to improve the classification performance against new types of attacks by classifying traffic with a probability below a certain level as attacks, and demonstrate the efficiency of our approach through experiments.

• Review of KIISC
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• v.19 no.3
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• pp.46-57
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• 2009

분산 서비스 거부(DDoS: Distributed Denial of Service) 공격이 인터넷에 대하여 거대한 위협을 제공하고 있으며, 이에 대한 대응책들이 많이 제시되었다. 그러나 공격의 복잡성과 다양성으로 인하여 어떤 대응 기법이 효과적인지도 상당히 혼란스럽게 되었다. 공격자들은 보안 시스템을 우회하기 위하여 꾸준히 공격도구들을 변경하고 있으며, 이에 대한 방패로써 연구자들 역시 새로운 공격에 대한 대응책을 강구하고 있다. 따라서 본 논문에서는 DDoS 기술동향, DDoS 공격 및 대응 기법에 대한 분류법 및 DDoS 대응 기법의 과제에 대하여 기술하고자 한다. 이를 통하여 효과적인 DDoS 공격 대응책을 수립하는데 필요한 기초 자료로 활용하고자 한다.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.97-116
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• 2003

Frequency of attacks on web services and the resulting damage continue to grow as web services become popular. Techniques used in web service attacks are usually different from traditional network intrusion techniques, and techniques to protect web services are badly needed. Unfortunately, conventional intrusion detection systems (IDS), especially those based on known attack signatures, are inadequate in providing reasonable degree of security to web services. An application-level IDS, tailored to web services, is needed to overcome such limitations. The first step in developing web application IDS is to analyze known attacks on web services and characterize them so that anomaly-based intrusion defection becomes possible. In this paper, we classified known attack techniques to web services by analyzing causes, locations where such attack can be easily detected, and the potential risks.

• 한국IT서비스학회:학술대회논문집
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• 2009.05a
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• pp.214-217
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• 2009

통신망의 발달로 수많은 인터넷 기반 서비스들이 등장함에 따라 다양한 외부공격이 심화되고 있다. 특히, 시스템 또는 네트워크 자원을 공격 대상으로 하는 서비스 거부 공격(DoS : Denial of Service) 및 분산 서비스 거부 공격(DDoS : Distributed DoS)의 문제가 대두되고 있는데, 본 논문에서는 DDoS 공격 근원지 IP주소의 위치 분류의 필요성을 분석하고 공격 근원지 IP주소 위치의 국내 외 여부를 판별하기 위해 국내 IP분배 할당 체계 현황을 분석한다. 또한 DDoS공격을 포함한 여러 가지 해킹에 빠르게 대응할 수 있고 근원지 IP에 관련된 정보를 알아낼 수 있는 시스템을 위한 분류 기법 정립 방안을 제시한다.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.99-108
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• 2010

The main contents of this paper is to develope effective measures for Internet Web service attack, classifying vulnerability of Web Service by network layer and host unit and researching classification method by attack range of type of services. Using this paper, we can accumulate analyzed Web service attack information which is key information of promote Web security strengthening business, and basis of relevant security research for detect and response Web site attack which can contribute to activation information security industry.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.871-879
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• 2020

Perceptual Ad-Blocking is a new advertising blocking technique that detects online advertising by using an artificial intelligence-based advertising image classification model. A recent study has shown that these Perceptual Ad-Blocking models are vulnerable to adversarial attacks using adversarial examples to add noise to images that cause them to be misclassified. In this paper, we prove that existing perceptual Ad-Blocking technique has a weakness for several adversarial example and that Defense-GAN and MagNet who performed well for MNIST dataset and CIFAR-10 dataset are good to advertising dataset. Through this, using Defense-GAN and MagNet techniques, it presents a robust new advertising image classification model for adversarial attacks. According to the results of experiments using various existing adversarial attack techniques, the techniques proposed in this paper were able to secure the accuracy and performance through the robust image classification techniques, and furthermore, they were able to defend a certain level against white-box attacks by attackers who knew the details of defense techniques.