• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.1-10
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• 2018

Network threats such as Internet worms and computer viruses have been significantly increasing. In particular, APTs(Advanced Persistent Threats) and ransomwares become clever and complex. IDSes(Intrusion Detection Systems) have performed a key role as information security solutions during last few decades. To use an IDS effectively, IDS rules must be written properly. An IDS rule includes a key signature and is incorporated into an IDS. If so, the network threat containing the signature can be detected by the IDS while it is passing through the IDS. However, it is challenging to find a key signature for a specific network threat. We first need to analyze a network threat rigorously, and write a proper IDS rule based on the analysis result. If we use a signature that is common to benign and/or normal network traffic, we will observe a lot of false alarms. In this paper, we propose a scheme that analyzes a network threat and extracts key signatures corresponding to the threat. Specifically, our proposed scheme quantifies the degree of correspondence between a network threat and a signature using the LDA(Latent Dirichlet Allocation) algorithm. Obviously, a signature that has significant correspondence to the network threat can be utilized as an IDS rule for detection of the threat.

• Korean Security Journal
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• no.15
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• pp.337-358
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• 2008

With the Japanese attacks on Pearl Harbor in 1941, approximately 2,500 people were killed. The terrorist attack on World Trade Center in the United States resulted in the heavy loss of people's lives, 2,749 in all. The 9.11 demonstrated that terrorist attack could be more serious problem than the war in our modern life. In addition, terrorist armed with new and high technologies have become more dangerous elements to the international community. Especially, the fact that the weapons of mass destruction are used by terrorist organizations is a matter of great concern. The strength of terrorist arsenal gives terrorist a decided advantage over us. The chances of success for terrorist have been increased due to the terrorist friendly environments. Terrorism has evolved without stopping from its birth, which is imposing a great burden on the authorities concerned. The counter-terrorism strategy and tactics used in the past have been useless in the fighting against new terrorism. To cope with the fast changing terrorism, comprehensive countermeasures should be developed. The purpose of this study is to know the enemy. To achieve the goal, the current situation on international terrorism as a whole is examined. Based on the result of the research, this paper also tried to give a perspectives on the future terrorism. At the same time, it provides a guidelines of the direction in the fighting against terrorism.

• Review of KIISC
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• v.18 no.5
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• pp.33-48
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• 2008

최근 은행의 소유지분한도와 설립자본금 등에 대한 정부의 금융규제 완화로 인터넷전문은행의 설립 가능성이 높아지고 있다. 그러나 우리나라의 전자금융환경은 전자금융거래법 제정에 따라 금융기관의 입증책임을 강화함으로써 금융기관의 전자금융리스크가 상대적으로 크게 증가하였다. 또한, 정보 공격기술 및 수법의 발달로 전자금융보안에 대한 위협이 지속적으로 증가하고 있다. 이외 에도 신BIS 리스크 평가에 IT운영리스크가 포함되는 등 금융환경 변화 및 정보통신 기술의 발전으로 인한 전자금융리스크가 계속 확대되고 있는 추세에 있다. 이러한 금융환경 변화와 함께 서비스채널이 인터넷에 집중되는 인터넷전문은행은 기존의 전통적인 은행과 차별되는 리스크에 추가적으로 노출될 위험성이 높다. 이러한 리스크에 대한 인식 및 대비 부재는 금융소비자가 금융권 전자금융거래에 대한 불신으로 확산되거나, 금융시장의 불안정성을 야기하는 금융사고로 이어져 자칫 국내 전자금융의 발전을 저해하는 심각한 요소가 될 수 있다. 본 논문에서는 국내 금융환경과는 차이가 있지만, 인터넷전문은행이 가져올 전자금융의 기술적 변화는 유사하다는 점에서 해외 주요국가의 인터넷전문은행 현황과 전자금융부문을 중심으로 인터넷전문은행 설립인가 사례를 살펴보고, 국내에서 인터넷전문은행 설립시 우리가 취해야 할 입장에 대해 시사점을 얻고자 하였다. 그리고 국내 전자금융 환경에서 전통적인 일반은행과 차별되거나 인터넷전문은행 고유의 특성으로 발생되는 주요 전자금융리스크를 다섯 가지로 분석하였고, 이러한 전자금융리스크를 줄이기 위한 대응방안을 모색해 보았다. 정부의 금융규제 완화는 금융자유화를 진전시켜 금융거래가 자유경쟁원리에 입각해 이루어짐에 따라 국민경제의 발전에 있어서 바람직한 결과를 얻고자 하는 것이다. 그러나 다른 한편으로 과도한 리스크에 노출 될 경우에는 금융시장의 불안정성을 야기하고 이로 인해 역 선택과 도덕적 해이를 야기 시키는 등 여러 가지의 폐해를 줄 수도 있다 이러한 폐해를 줄이기 위해서는 인터넷전문은행의 고유한 특성으로 수반되는 리스크와 상대적으로 그 중요성이 부각되는 전자금융리스크에 대한 관리 감독을 강화해야 한다. 또한 이러한 리스크 관리강화를 위한 제도적 장치는 인터넷전문은행의 자율성과 책임성을 부여하는 방향으로 이루어지는 것이 바람직하다. 인터넷전문은행이 실질적으로 다수의 금융이용자에게 다양한 혜택과 효율적인 금융서비스를 제공하기 위해서는 초기 사업계획 심사 단계에서부터 위험성이 크게 증가하는 전자금융리스크에 대해서, 적절한 관리방안 수립을 통해 예상되는 리스크를 줄이기 위한 노력이 필요하다고 생각한다. 그리고 인터넷전문은행에 대한 구체적인 인가요건이 마련되지 못한 현 상황에서, 국내 인터넷전문은행 설립이 우리나라 전자금융거래에 발전적 역할을 할 수 있도록 앞으로 더 많은 논의와 연구가 진행될 필요가 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1147-1157
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• 2014

Intelligent transportation system (ITS) is realized through a highly ephemeral network, i.e. vehicular ad hoc network (VANET) which is on its way towards the deployment stage, thanks to the advancements in the automobile and communication technologies. However, it has not been successful, at least to date, to install the technology in the mass of vehicles due to security and privacy challenges. Besides, the users of such technology do not want to put their privacy at stake as a result of communication with peer vehicles or with the infrastructure. Therefore serious privacy measures should be taken before bringing this technology to the roads. To date, privacy issues in ephemeral networks in general and in VANET in particular, have been dealt with through various approaches. So far, multiple pseudonymous approach is the most prominent approach. However, recently it has been found out that even multiple pseudonyms cannot protect the privacy of the user and profilation is still possible even if different pseudonym is used with every message. Therefore, another privacy-aware mechanism is essential in vehicular networks. In this paper, we propose a novel identity exchange mechanism to preserve conditional privacy of the users in VANET. Users exchange their pseudonyms with neighbors and then use neighbors' pseudonyms in their own messages. To this end, our proposed scheme conditionally preserves the privacy where the senders of the message can be revoked by the authorities in case of any dispute.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.789-801
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• 2018

Currently widely used IP cameras provide the ability to control IP cameras remotely via mobile devices. To do so, the IP camera software is installed on the website specified by the camera manufacturer, and authentication is performed through the password between the IP camera and the mobile device. However, many products currently used do not provide a secure channel between the IP camera and the mobile device, so that all IDs and passwords transmitted between the two parties are exposed. To solve these problems, we propose an authentication and key exchange protocol using ID-based signature scheme. The proposed protocol is characterized in that (1) mutual authentication is performed using ID and password built in IP camera together with ID-based signature, (2) ID and password capable of specifying IP camera are not exposed, (3) provide forward-secrecy using Diffie-Hellman key exchange, and (4) provide security against external attacks as well as an honest-but-curious manufacturer with the master secret key of the ID-based signature.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.238-240
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• 2006

네트워크상에서 발생하는 다양한 형태의 대량의 데이터를 정확하고 효율적으로 분석하기 위해 설계되고 있는 마이닝 시스템들은 목표지향적으로 훈련데이터들을 어떻게 구축하여 다룰 것인지에 대한 문제보다는 대부분 얼마나 많은 데이터 마이닝 기법을 지원하고 이를 적용할 수 있는지 등의 기법에 초점을 두고 있다. 따라서, 점점 더 에이전트화, 분산화, 자동화 및 은닉화 되는 최근의 보안공격기법을 정확하게 탐지하기 위한 방법은 미흡한 실정이다. 본 연구에서는 유비쿼터스 환경 내에서 발생 가능한 문제 중 복잡하고 지능화된 침입패턴의 탐지를 위해 데이터 마이닝 기법과 결함허용방법을 이용하는 개선된 학습알고리즘과 후처리 방법에 의한 RTPID(Refinement Training and Post-processing for Intrusion Detection)시스템을 제안한다. 본 논문에서의 RTPID 시스템은 active learning과 post-processing을 이용하여, 네트워크 내에서 발생 가능한 침입형태들을 정확하고 효율적으로 다루어 분석하고 있다. 이는 기법에만 초점을 맞춘 기존의 데이터마이닝 분석을 개선하고 있으며, 특히 제안된 분석 프로세스를 진행하는 동안 능동학습방법의 장점을 수용하여 학습효과는 높이며 비용을 감소시킬 수 있는 자가학습방법(self learning)방법의 효과를 기대할 수 있다. 이는 관리자의 개입을 최소화하는 학습방법이면서 동시에 False Positive와 False Negative 의 오류를 매우 효율적으로 개선하는 방법으로 기대된다. 본 논문의 제안방법은 분석도구나 시스템에 의존하지 않기 때문에, 유사한 문제를 안고 있는 여러 분야의 네트웍 환경에 적용될 수 있다.더욱 높은성능을 가짐을 알 수 있다.의 각 노드의 전력이 위험할 때 에러 패킷을 발생하는 기법을 추가하였다. NS-2 시뮬레이터를 이용하여 실험을 한 결과, 제안한 기법이 AOMDV에 비해 경로 탐색 횟수가 최대 36.57% 까지 감소되었음을 알 수 있었다.의 작용보다 더 강력함을 시사하고 있다.TEX>로 최고값을 나타내었으며 그 후 감소하여 담금 10일에는 $1.61{\sim}2.34%$였다. 시험구간에는 KKR, SKR이 비교적 높은 값을 나타내었다. 무기질 함량은 발효기간이 경과할수록 증하였고 Ca는 $2.95{\sim}36.76$, Cu는 $0.01{\sim}0.14$, Fe는 $0.71{\sim}3.23$, K는 $110.89{\sim}517.33$, Mg는 $34.78{\sim}122.40$, Mn은 $0.56{\sim}5.98$, Na는 $0.19{\sim}14.36$, Zn은 $0.90{\sim}5.71ppm$을 나타내었으며, 시험구별로 보면 WNR, BNR구가 Na만 제외한 다른 무기성분 함량이 가장 높았다.O to reduce I/O cost by reusing data already present in the memory of other nodes. Finally, chunking and on-line compression mechanisms are included in both models. We demonstrate that we can obtain significantly high-performanc

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.295-310
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• 2014

DCS (Distributed Control System), the main control system of power plants, is an automated system for enhancing operational efficiency by monitoring, tuning and real-time operation. DCS is becoming more intelligent and open systems as Information technology are evolving. In addition, there are a large amount of investment to enable proactive facility management, maintenance and risk management through the predictive diagnostics. However, new upcoming weaponized malware, such as Stuxnet designed for disrupting industrial control system(ICS), become new threat to the main control system of the power plant. Even though these systems are not connected with any other outside network. The main control systems used in the power plant usually have been used for more than 10 years. Also, this system requires the extremely high availability (rapid recovery and low failure frequency). Therefore, installing updates including security patches is not easy. Even more, in some cases, installing security updates can break the warranty by the vendor's policy. If DCS is exposed a potential vulnerability, serious concerns are to be expected. In this paper, we conduct the penetration test by using NESSUS, a general-purpose vulnerability scanner under the simulated environment configured with the Ovation version 1.5. From this result, we suggest a log analysis method to detect the security infringement and react the incident effectively.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.12
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• pp.471-480
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• 2016

Recently, the damage of cyber attack toward infra-system, national defence and security system is gradually increasing. In this situation, military recognizes the importance of cyber warfare, and they establish a cyber system in preparation, regardless of the existence of threaten. Thus, the study of Intrusion Detection System(IDS) that plays an important role in network defence system is required. IDS is divided into misuse and anomaly detection methods. Recent studies attempt to combine those two methods to maximize advantagesand to minimize disadvantages both of misuse and anomaly. The combination is called Hybrid IDS. Previous studies would not be inappropriate for near real-time network environments because they have computational complexity problems. It leads to the need of the study considering the structure of IDS that have high detection rate and low computational cost. In this paper, we proposed a Hybrid IDS which combines C4.5 decision tree(misuse detection method) and Weighted K-means algorithm (anomaly detection method) hierarchically. It can detect malicious network packets effectively with low complexity by applying mutual information and genetic algorithm based efficient feature selection technique. Also we construct upgraded the the hierarchical structure of IDS reusing feature weights in anomaly detection section. It is validated that proposed Hybrid IDS ensures high detection accuracy (98.68%) and performance at experiment section.

• 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 Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.