• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.905-917
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• 2018

The sovereign state has the right to engage in self-defense or war with the approval of the Security Council when it receives an invasion of territory from a foreign country. War is conducted under the principle of the necessity and proportionality of self-defense. In case of cyber attack, proportional countermeasure must be made through attack means and effect analysis, and cyber weapons need to be classified for this purpose. Therefore, this study aims to provide a rational and legitimate response according to the necessity and proportionality of the self - defense right by suggesting definition and classification criteria of cyber weapons. In this study, cyber weapons were defined as "means of collecting, attacking, and protecting information using cyber technology in the cyber space according to military objectives. Based on existing weapon systems and public cyber weapons cases, cyber weapons were classified as (1) cyber weapons for information gathering, (2) cyber weapons for attack, and (3) cyber weapons for protection. We suggest the considerations for applying the proportional response according to this functional classification. In order to guarantee the principle of proportionality to cyber attacks in the future, the classification study based on the cyber weapon effect should be conducted. This study has conducted an exploratory study on the classification of cyber clusters which constitutes one axis of the proportionality principle.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.314-317
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• 2003

기존의 침입탐지 시스템에서는 오용탐지모델이 널리 사용되고 있다. 이 모델은 낮은 오판율(False Alarm rates)을 가지고 있으나, 새로운 공격에 대해 전문가시스템(Expert Systems)에 의한 규칙추가를 필요로 한다. 그리고 그 규칙과 완전히 일치되는 시그너처만 공격으로 탐지하므로 변형된 공격을 탐지하지 못한다는 문제점을 가지고 있다 본 논문에서는 이러한 문제점을 보완하기 위해 주성분분석(Principle Component Analysis; 이하 PCA)과 서포트 벡터 머신(Support Vector Machines; 이하 SVM)을 이용한 침입탐지 시스템을 제안한다. 네트워크 상의 패킷은 PCA를 이용하여 결정된 주성분 공간에서 해석되고, 정상적인 흐름과 비정상적인 흐름에 대한 패킷이미지패턴으로 정규화 된다. 이러한 두 가지 클래스에 대한 SVM 분류기를 구현한다. 개발하는 침입탐지 시스템은 알려진 다양한 침입유형뿐만 아니라, 새로운 변종에 대해서도 분류기의 유연한 반응을 통하여 효과적으로 탐지할 수 있다.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.91-97
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• 2020

Recently, phishing attacks targeting those involved in defense, security and unification have been on the rise. In particular, hacking attack organization Kimsuky has been engaged in activities to collect important information from public organizations through phishing attacks since 2013. In this paper, profiling analysis of phishing mail attack organization was performed. Through this process, we estimated the purpose of the attack group and suggested countermeasures.

• The Journal of the Korea Contents Association
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• v.3 no.4
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• pp.48-54
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• 2003

Recently it frequently occur that remote host or network device breaks down because of various traffic flooding attacks. This kind of attack is classified an one of the most serious attacks of it can be used to a need of other hackings. This research is gathering system's informations for detecting a traffic flooding attack using the SNMP MIB. We analyze the traffic characteristic applying the critical value commonly used in analytical procedure of traffic flooding attacks. As a result or this analysis, traffic flooding attacks have a special character of its on. The proposed algorithm in this paper would be more available to a previous detecting method and a previous protecting method.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.101-108
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• 2022

We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.82-84
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• 2005

기존의 웜 탐지는 중요한 네트워크 소스의 폭주라 스위치 라우터 및 말단 시스템에서의 변동 효과를 가지고 공격을 판단했었다. 하지만 최근의 인터넷 웜은 발생 초기에 대응하지 못하면 그 피해의 규모가 기하급수적으로 늘어난다. 또한 방어하기가 어려운 서비스 거부 공격을 일으킬 수 있는 간접 공격의 주범이 될 수 있다는 정에서 웜의 탐지와 방어는 인터넷 보안에 있어서 매우 중요한 사안이 되었다. 본 논문에서는 이미 알려진 공격뿐 아니라 새로운 웜의 스캐닝 공격을 탐지하기 위하여, 패턴 분류 문제에 있어서 우수한 성능을 보이는 Support Vector Machine(SVM)[1]을 사용하여 인터넷 웜의 스캐닝 공격을 탐지하는 시스템 모델을 제안한다.

• Review of KIISC
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• v.13 no.4
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• pp.88-101
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• 2003

인터넷과 전자상거래의 발전에 따라 전자적 수단으로 화폐 가치를 이전하는 수단이 마그네틱 카드에서 IC카드로 대체될 것으로 기대되고 있다. IC카드는 마그네틱 카드보다 많은 데이터를 저장할 수 있고, 물리적인 보안(Tamper-resistance)과 암호기법(Cryptographic Technique)을 통해 외부와의 통신을 엄격히 통제 및 보호할 수 있는 장점이 있다. 본 고에서는 IC카드의 안전성에 대해 알아보기 위해 먼저 IC카드를 정의하고 분류한다. 그리고, IC카드의 안전성 관련기능으로 카드에서의 사용자 인증, 카드와 카드단말사이의 실체인증, 접근통제 및 데이터의 기밀성/무결성, 키관리에 대해 알아보고, IC카드와 관련된 공격모델 및 공격기법들을 조사하여 정리하고자 한다.

• Review of KIISC
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• v.30 no.1
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• pp.43-53
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• 2020

딥러닝 기술의 발달로 인해 다양한 응용 분야에서 해당 기술 활용 시 좋은 성능을 보임에 따라 부채널 분석 분야에서도 딥러닝 기술을 적용하는 연구들이 활발히 진행되고 있다. 초기 딥러닝 기술은 데이터 분류 문제를 해결해야 하는 템플릿 공격과 같은 프로파일링 기반의 부채널 공격에 집중되어 적용되었지만 최근에는 프로파일링 기반의 부채널 분석 뿐만 아니라 상관 전력 분석 등과 같은 논프로파일링 기반 부채널 공격, 파형 인코딩 및 전처리, 부채널 누출신호 탐색 등으로 연구범위가 확대되어지고 있다. 본 논문에서는 딥러닝을 이용한 부채널 분석 기술의 최신 연구 동향을 분야별로 체계적으로 정리 및 분석하고자 한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.249-256
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• 2024

Machine learning-based intrusion detection methodologies require a large amount of uniform learning data for each class to be classified, and have the problem of having to retrain the entire system when adding an attack type to be detected or classified. In this paper, we use feature learning and hierarchical classification methods to solve classification problems and data imbalance problems using relatively little training data, and propose an intrusion detection methodology that makes it easy to add new attack types. The feasibility of the proposed system was verified through experiments using KDD IDS data..

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.1-8
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• 2020

Recently, the number of APT(Advanced Persistent Threats) attack using malware has been increasing, and research is underway to prevent and detect them. While it is important to detect and block attacks before they occur, it is also important to make an effective response through an accurate analysis for attack case and attack type, these respond which can be determined by analyzing the attack group of such attacks. Therefore, this paper propose a framework based on genetic algorithm for analyzing malware and understanding attacker group's features. The framework uses decompiler and disassembler to extract related code in collected malware, and analyzes information related to author through code analysis. Malware has unique characteristics that only it has, which can be said to be features that can identify the author or attacker groups of that malware. So, we select specific features only having attack group among the various features extracted from binary and source code through the authorship clustering method, and apply genetic algorithm to accurate clustering to infer specific features. Also, we find features which based on characteristics each group of malware authors has that can express each group, and create profiles to verify that the group of authors is correctly clustered. In this paper, we do experiment about author classification using genetic algorithm and finding specific features to express author characteristic. In experiment result, we identified an author classification accuracy of 86% and selected features to be used for authorship analysis among the information extracted through genetic algorithm.