• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.95-105
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• 2018

As the IT industry developed, the information held by the company soon became a corporate asset. As this information has value as an asset, the number and scale of various cyber attacks which targeting enterprises and institutions is increasing day by day. Therefore, research are being carried out to protect the assets from cyber attacks by using the attack graph to identify the possibility and risk of various attacks in advance and prepare countermeasures against the attacks. In the attack graph, security metric is used as a measure for determining the importance of each asset or the risk of an attack. This is a key element of the attack graph used as a criterion for determining which assets should be protected first or which attack path should be removed first. In this survey, we research trends of various security metrics used in attack graphs and classify the research according to application viewpoints, use of CVSS(Common Vulnerability Scoring System), and detail metrics. Furthermore, we discussed how to graft the latest security technologies, such as MTD(Moving Target Defense) or SDN(Software Defined Network), onto the attack graphs.

• 정보보호학회논문지
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• 제28권2호
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• pp.485-500
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• 2018

시스템 및 네트워크 환경의 규모가 확대되고, 네트워크 구조 및 시스템 구성이 빈번하게 변화함에 따라 네트워크 관리자가 현황을 수동으로 관리하고 실시간 변동사항을 식별하는 데에 많은 어려움이 발생하고 있다. 본 논문에서는 동적인 네트워크 정보를 실시간으로 스캔하고, 사전에 수집한 취약점 정보를 바탕으로 네트워크 내 장치의 취약성 정도를 점수화하고 최종적으로 공격자의 입장에서 공격 가능한 모든 경로를 도출하여 네트워크 관리자에게 공격 가능성이 높은 경로 목록을 제공하는 알고리즘을 제안하였다. 또한 제안하는 알고리즘을 토대로 한 Attack Graph를 실제로 구현하였으며, Software Defined Networking (SDN) 환경이 포함된 동적으로 변화하는 가상 네트워크 환경을 구축한 후 시뮬레이션을 진행하여 Moving Target Defense (MTD) 개념이 반영된 시스템에도 적용이 가능함을 입증하였다.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.75-84
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• 2018

Social engineering attack means to get information of Social engineering attack means to get information of opponent without technical attack or to induce opponent to provide information directly. In particular, social engineering does not approach opponents through technical attacks, so it is difficult to prevent all attacks with high-tech security equipment. Each company plans employee education and social training as a countermeasure to prevent social engineering. However, it is difficult for a security officer to obtain a practical education(training) effect, and it is also difficult to measure it visually. Therefore, to measure the social engineering threat, we use the results of social engineering training result to calculate the risk by system asset and propose a attack graph based probability. The security officer uses the results of social engineering training to analyze the security threats by asset and suggests a framework for quick security response. Through the framework presented in this paper, we measure the qualitative social engineering threats, collect system asset information, and calculate the asset risk to generate probability based attack graphs. As a result, the security officer can graphically monitor the degree of vulnerability of the asset's authority system, asset information and preferences along with social engineering training results. It aims to make it practical for companies to utilize as a key indicator for establishing a systematic security strategy in the enterprise.

• 디지털산업정보학회논문지
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• 제16권1호
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• pp.79-92
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• 2020

As the number of systems increases and the network size increases, automated attack prediction systems are urgently needed to respond to cyber attacks. In this study, we developed four types of information gathering sensors for collecting asset and vulnerability information, and developed technology to automatically generate attack graphs and predict attack targets. To improve performance, the attack graph generation method is divided into the reachability calculation process and the vulnerability assignment process. It always keeps up to date by starting calculations whenever asset and vulnerability information changes. In order to improve the accuracy of the attack target prediction, the degree of asset risk and the degree of asset reference are reflected. We refer to CVSS(Common Vulnerability Scoring System) for asset risk, and Google's PageRank algorithm for asset reference. The results of attack target prediction is displayed on the web screen and CyCOP(Cyber Common Operation Picture) to help both analysts and decision makers.

• 정보보호학회논문지
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• 제31권5호
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• pp.1021-1030
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• 2021

본 논문은 현대의 복잡한 사이버 공격을 모사하는 훈련 시나리오를 효과적으로 표현하기 위한 모델인 S-CAFG(Stage-based Cyber Attack Flow Graph)를 제안하고 평가한다. 이 모델은 더 복잡한 시나리오 표현을 위해 기존 그래프 및 트리 모델을 결합하고 stage 노드를 도입했다. 평가는 기존 모델링 기법으로는 표현하기 어려운 시나리오를 제작하고 이를 S-CAFG로 모델링하는 방식으로 진행했다. 평가 결과, S-CAFG는 동시 공격, 부가적 공격, 우회 경로 선택 등 매우 복잡한 공격 시나리오를 효과적으로 표현할 수 있음을 확인했다.

• 한국컴퓨터정보학회논문지
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• 제28권5호
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• pp.57-66
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• 2023

본 논문에서는 Enterprise 네트워크 이외 환경에서의 공격 그래프 연구 중 최근 5년간 가장 많이 연구된 사이버-물리 시스템(CPS) 환경에 대한 공격 그래프 연구 동향을 살펴보고, 기존 연구의 한계와 앞으로 나아갈 방향을 분석한다. 최근 5년간 발표된 공격 그래프 논문 150여 편 중 35편이 CPS 환경을 대상으로 하고 있으며, 본 논문에서는 CPS 환경의 보안 측면 특징을 살펴보고, 대상 연구들을 이러한 특징들에 따라 물리 시스템 모델링 여부와 네트워크 단절 구간에 대한 고려 여부의 두 가지 관점으로 분류 및 분석한다. 본 논문에서 소개한 20편의 논문 중 절반이 CPS 환경의 특징을 제대로 반영하지 못하며, 나머지 절반의 연구가 물리 시스템 모델링과 네트워크 단절 구간 중 하나씩을 다루고 있다. 본 논문에서는 이러한 상황을 바탕으로 CPS 환경에서의 공격 그래프 연구가 직면한 어려움을 진단하고 이에 따라 앞으로의 CPS 환경 공격 그래프 연구는 국가주도 연구, 공개된 상용 시스템을 대상으로 한 연구가 주를 이룰 것으로 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2149-2170
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• 2017

Application-layer Distributed Denial-of-Service (DDoS) attack is one of the leading security problems in the Internet. In recent years, the attack strategies of application-layer DDoS have rapidly developed. This paper introduces a new attack strategy named Path Vulnerabilities-Based (PVB) attack. In this attack strategy, an attacker first analyzes the contents of web pages and subsequently measures the actual response time of each webpage to build a web-resource-weighted-directed graph. The attacker uses a Top M Longest Path algorithm to find M DDoS vulnerable paths that consume considerable resources when sequentially accessing the pages following any of those paths. A detection mechanism for such attack is also proposed and discussed. A finite-state machine is used to model the dynamical processes for the state of the user's session and monitor the PVB attacks. Numerical results based on real-traffic simulations reveal the efficiency of the attack strategy and the detection mechanism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권5호
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• pp.1920-1937
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• 2015

Network environment has been under constant threat from both malicious attackers and inherent vulnerabilities of network infrastructure. Existence of such threats calls for exhaustive vulnerability analyzing to guarantee a secure system. However, due to the diversity of security hazards, analysts have to select from massive alternative hardening strategies, which is laborious and time-consuming. In this paper, we develop an approach to seek for possible hardening strategies and prioritize them to help security analysts to handle the optimal ones. In particular, we apply a Risk Flow Attack Graph (RFAG) to represent network situation and attack scenarios, and analyze them to measure network risk. We also employ a multi-objective genetic algorithm to infer the priority of hardening strategies automatically. Finally, we present some numerical results to show the performance of prioritizing strategies by network risk and hardening cost and illustrate the application of optimal hardening strategy set in typical cases. Our novel approach provides a promising new direction for network and vulnerability analysis to take proper precautions to reduce network risk.

• 한국멀티미디어학회논문지
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• 제23권1호
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• pp.24-30
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• 2020

As the number of internet-connected appliances and the variety of IoT services are rapidly increasing, it is hard to protect IT assets with traditional network security techniques. Most traditional network log analysis systems use rule based mechanisms to reduce the raw logs. But using predefined rules can't detect new attack patterns. So, there is a need for a mechanism to reduce congested raw logs and detect new attack patterns. This paper suggests enterprise security management for IoT services using graph and network measures. We model an event network based on a graph of interconnected logs between network devices and IoT gateways. And we suggest a network clustering algorithm that estimates the attack probability of log clusters and detects new attack patterns.