• 융합보안논문지
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• 제14권6_2호
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• pp.45-52
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• 2014

현대에 이르러 우리나라의 경비 형태는 과거 인력 경비에 비해, 보다 효율적인 기계경비 형태로 발전하고 있다. 기계경비는 경제적, 운용적 측면에서 인력경비에 비해 그 장점을 가지고 있다. 하지만 기계경비에 사용되는 장비를 운용하는데 있어서 오경보 등과 같은 취약점이 발생하고 있으며, 이를 보완하기 위한 연구가 진행되고 있다. 이에 본 논문은 기계경비 운용상의 취약점에 대하여 AHP기법을 통하여 도출된 결과에 대한 개선방안을 제시하고자 한다.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• 융합보안논문지
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• 제12권5호
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• pp.71-77
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• 2012

경비의 형태는 과거 인력 경비에서 현대에 이르러 기계경비로 점차 전환되고 있다. 이는 기계경비가 인력경비에 비해 효율적이기 때문이다. 하지만 기계경비 시스템의 운용에 있어 오경보로 인해 기계경비의 높은 기대효과에도 불구하고 발전을 저해하는 요소로 인해 기계경비의 성장을 더디게 하고 있다. 이에 본 논문은 연구는 IPA(Importance Performance Analysis)기법을 이용하여 기계경비 시스템 운용에 있어 결함성 측면의 제거가 기계경비의 발전에 있어 그 중요도가 얼마나 높은지를 살펴보고, 또한 기술적 측면에서 기계경비 시스템의 오동작을 최소화할 수 있는 복합형 방범 센서를 제시하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.305-325
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• 2014

In cloud computing infrastructure, current virtual machine trust measurement methods have many shortcomings in dynamism, security and concurrency. In this paper, we present a new method to measure the trust of virtual machine. Firstly, we propose "behavior trace" to describe the state of virtual machine. Behavior trace is a sequence of behaviors. The measurement of behavior trace is conducted on the basis of anticipated trusted behavior, which not only ensures security of the virtual machine during runtime stage but also reduces complexity of the trust measurement. Based on the behavior trace, we present a Dynamic Tree Style Trust Measurement Model (DTSTM). In this model, the measurement of system domain and user domain is separated, which enhances the extensibility, security and concurrency of the measurement. Finally, based on System Call Interceptor (SCI) and Virtual Machine Introspection (VMI) technology, we implement a DTSTM prototype system for virtual machine trust measurement. Experimental results demonstrate that the system can effectively verify the trust of virtual machine and requires a relatively low performance overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권2호
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• pp.498-514
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• 2012

Machine to machine (M2M) communications is the hottest issue in the standardization and industry area, it is also defined as machine-type communication (MTC) in release 10 of the 3rd Generation Partnership Project (3GPP). Recently, most research have focused on congestion control, sensing, computing, and controlling technologies and resource management etc., but there are few studies on security aspects. In this paper, we first introduce the threats that exist in M2M system and corresponding solutions according to 3GPP. In addition, we present several new security issues including group access authentication, multiparty authentication and data authentication, and propose corresponding solutions through modifying existing authentication protocols and cryptographic algorithms, such as group authentication and key agreement protocol used to solve group access authentication of M2M, proxy signature for M2M system to tackle authentication issue among multiple entities and aggregate signature used to resolve security of small data transmission in M2M communications.

• 한국멀티미디어학회논문지
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• 제23권11호
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• 한국IT서비스학회지
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• 제19권2호
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• pp.83-92
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• 2020

The current technology drain at small and medium-sized enterprises in Korea is very serious. According to the National Intelligence Service's survey data, 69 percent of technology leaks are made through employees of small and medium-sized enterprises. A document security system was introduced to compensate for the problem. However, small and medium-sized enterprises are not doing well due to their poor environment. Therefore, it proposes a document security system suitable for small businesses by developing a location information machine learning system that automatically creates a document security Green Zone through learning, and an ECM-based electronic document leakage prevention system that manages generated Green Zone information by reflecting it into the document authority system. And step by step, propose a universal solution through cloud services..

• 한국융합학회논문지
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• 제1권1호
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• pp.49-55
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• 2010

정보통신기술 발전에 따라 많은 장치들간의 통신과 네트워킹의 수용이 이뤄지고 있다. 장치간의 통신을 위한 융합 사업이 빠르게 발전되어지고 있다. IT 융합 통신은 무선통신에서 차후 개척분야의 하나로 여겨지고 있다. 본 논문에서는 IT 융합 구조에서 M2M, 지능형 자동차, 스마트그리드, U-헬스케어에 대한 보안 위협요소를 분석하였다. 임베디드 시스템 보안, 포렌식 보안, 사용자 인증과 키관리 기법에 대한 IT 융합 보안의 방향을 제안하였다.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• 디지털콘텐츠학회 논문지
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• 제18권4호
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• pp.707-712
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• 2017

데이터의 양이 기하급수적으로 증가함에 따라 추천 시스템(recommender system)은 영화, 도서, 음악 등 다양한 산업에서 관심을 받고 있고 연구 대상이 되고 있다. 추천시스템은 사용자들의 과거 선호도 및 클릭스트림(click stream)을 바탕으로 사용자에게 적절한 아이템을 제안하는 것을 목적으로 한다. 대표적인 예로 넷플릭스의 영화 추천 시스템, 아마존의 도서 추천 시스템 등이 있다. 기존의 선행 연구는 협업적 여과, 내용 기반 추천, 혼합 방식의 3가지 방식으로 크게 분류할 수 있다. 하지만 기존의 추천 시스템은 희소성(sparsity), 콜드스타트(cold start), 확장성(scalability) 문제 등의 단점들이 있다. 이러한 단점들을 개선하고 보다 정확도가 높은 추천 시스템을 개발하기 위해 실제 온라인 기업의 상품구매 데이터를 이용해 factorization machine으로 추천시스템을 설계했다.