• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.1-4
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• 2008

근년 일본, 캐나다, 호주, 미국, EU(주로 노르웨이, 영국) 등에서 인공위성을 이용한 해양 안전의 확보를 위한 연구개발이 진행되고 있으며, 일부 실해역 적용의 분야도 도출되고 있는 실정이다. 9.11테러 이후, 국제해사기구에서도 해상보안의 문제는 주요 이슈로 대두되어, 해상보안에의 활용 기술 개발이 먼저 시작되었다. 그 외, 밀입국 선박 감시 덴 해양오염 모니터링이 주요 활용분야이다. 간단하게 요약하면 다음과 같다. -노르웨이: Norwegian Defence Hesearch Establishment(NDRE)에서 주도적으로 선박 탐지 실험 및 기술 개발을 실시. 주로, ESA의 위성을 활용. 국가 보안의 목적으로는 적용을 하고 있음. -캐나다: 캐나다에서 소유하고 있는 RADARSAT을 이용하여 가장 많은 실험을 실시함. 영상을 처리하고 결과에 대한 평가를 수행하기 위한 시스템(Ocean Monitoring Workstation, OSM)을 개발하여 보급에 주력. -호주: 주로 캐나다의 위성 및 시스템의 적용을 하고 있음 영해 및 환경 감시의 역할을 수행. Coastwatch조직을 만들어 해상 감시활동을 하고 있음. -영국: 데이터 취득 후, 2.5시간 이내에 선박의 위치를 전송하는 인터페이스를 개발함. 일본의 경우, 다른 선진국에 비해서는 다소 늦게 시작되었다. 2003년 발간된 '재해 등에 대응한 인공위성이용기술에 관한 종합보고서'를 시작으로 정보수집위성 4기 및 지구관측위성을 이용한 해양 감시 활동이 시작되었다. 또한, 제 3기 과학기술기본계획(2006-2012)내에 해양 불법침입 탐지 기술 개발 항목이 반영되어 있다. 유럽의 해상보안서비스(MARISS)의 사용자 워크숍이 ESA ESRIN(이탈리아 프라스카티)에서 2008년 1월 22일 열렸다. 실질적인 내용은, '해상보안을 위한 우주 시스템'에 관한 것으로 인공위성 이용하는데 있어 설계안 및 데이터 이용 컨셉을 제시하는 것이었다. 여기서 중요한 것은 국가간의 협력이 절대적으로 필요하며, 기존의 시스템과의 통합에 있어 신뢰성을 어떻게 확보하는가에 있다고 할 수 있다. 또한, 보안과 환경모니터링의 기능이 분리되어 진행되고 있는 부분에 대한 정보 통합 방향도 제기되었다. 국내에서도 AIS와 SAR정보의 결합에 관한 검토는 이루어졌으며, 이를 바탕으로 EU와 같은 시스템의 구축(조직과 연구개발)을 위한 실질적인 검토가 필요하다.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.715-724
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• 2005

Recently many important systems that used to be operated in a closed environment are now providing web services and these kinds of web-based services are often an easy and common target of attacks. In addition, the great variety of web content and applications cause the development of new various intrusion technologies, while the misuse-based intrusion detection technology cannot keep the peace with the attacks and it seems to lack the capability to deal with such various new security threats, As a result it is necessary to research and develop new types of detection technologies that can detect newly developed attacks and intrusions as well as to be able to deal with previous types of exploits. In this paper, a HTTP traffic model is tested for its anomaly by using a HTTP request traffic pattern analysis and the field information analysis of the HTTP packet. Consequently, the HTTP traffic models by applying anomaly tests is designed and established.

• Smart Media Journal
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• v.4 no.4
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• pp.80-85
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• 2015

As the number of applications using the internet the rapidly increasing incidence of cyber attacks made on the internet has been increasing. In the equipment of L3 DDoS attack detection equipment in the world and incomplete detection of application layer based intelligent. Next-generation networks domestic product in high-performance wired and wireless network threat response techniques to meet the diverse requirements of the security solution is to close one performance is insufficient compared to the situation in terms of functionality foreign products, malicious code detection and signature generation research primarily related to has progressed malware detection and analysis of the research center operating in Window OS. In this paper, we describe the current status survey and analysis of the latest variety of new attack techniques and analytical skills with the latest cyber-attack analysis prejudice the security situation.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.313-319
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• 2015

As the number of applications using the internet the rapidly increasing incidence of cyber attacks made on the internet has been increasing. In the equipment of L3 DDoS attack detection equipment in the world and incomplete detection of application layer based intelligent. Next-generation networks domestic product in high-performance wired and wireless network threat response techniques to meet the diverse requirements of the security solution is to close one performance is insufficient compared to the situation in terms of functionality foreign products, malicious code detection and signature generation research primarily related to has progressed malware detection and analysis of the research center operating in Window OS. In this paper, we describe the current status survey and analysis of the latest variety of new attack techniques and analytical skills with the latest cyber-attack analysis prejudice the security situation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.647-660
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• 2022

Recent works demonstrate that the semi-supervised anomaly detection method functions quite well in the environment with normal data and some anomalous data. However, abnormal data shortages can occur in an environment where it is difficult to reserve anomalous data, such as an unknown attack in the cyber security fields. In this paper, we propose ADA-PH(Abnormal Data Augmentation Method using Perturbation based on Hypersphere), a novel anomalous data augmentation method that is applicable in an environment where abnormal data is insufficient to secure the performance of the semi-supervised anomaly detection method. ADA-PH generates abnormal data by perturbing samples located relatively far from the center of the hypersphere. With the network intrusion detection datasets where abnormal data is rare, ADA-PH shows 23.63% higher AUC performance than anomaly detection without data augmentation and even performs better than the other augmentation methods. Also, we further conduct quantitative and qualitative analysis on whether generated abnormal data is anomalous.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.185-192
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• 2006

By mistaking normal packets for harmful traffic, it may not offer service according to the intention of attacker with harmful traffic, because it is not easy to classify network traffic for normal service and it for DUoS(Distributed DoS) attack like the Internet worm. And in the IPv6 environment these researches on harmful traffic are weak. In this dissertation, hosts in the IPv6 environment are attacked by NETWIB and their attack traffic is monitored, then the statistical information of the traffic is obtained from MIB(Management Information Base) objects used in the IPv6. By adapting the ESM(Exponential Smoothing Method) to this information, a normal traffic boundary, i.e., a threshold is determined. Input traffic over the threshold is thought of as attack traffic.

• Annual Conference of KIPS
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• 2022.11a
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• pp.1056-1058
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• 2022

본 연구는 항만에서 안전 수칙을 위반하여 발생하는 사고 및 이상행동을 실시간 탐지를 수행한 후 위험 상황을 관리자가 신속하고 정확하게 대처할 수 있도록 지원하는 지능형 CCTV, Smart Eye를 제안한다. Smart Eye는 컴퓨터 비전(Computer Vision) 기반의 다양한 객체 탐지(Object Detection) 모델과 행동 인식(Action Recognition) 모델을 통해 낙하 및 전도사고, 안전 수칙 미준수 인원, 폭력적인 행동을 보이는 인원을 복합적으로 판단하며, 객체 추적(Object Tracking), 관심 영역(Region of Interest), 객체 간의 거리 측정 알고리즘을 구현하여, 제한구역 접근, 침입, 배회, 안전 보호구 미착용 인원 그리고 화재 및 충돌사고 위험도를 측정한다. 해당 연구를 통한 자동화된 24시간 감시체계는 실시간 영상 데이터 분석 및 판단 처리 과정을 거친 후 각 장소에서 수집된 데이터를 관리자에게 신속히 전달하고 항만 내 통합관제센터에 접목함으로써 효율적인 관리 및 운영할 수 있게 하는 '지능형 인프라'를 구축할 수 있다. 이러한 체계는 곧 스마트 항만 시스템 도입에 이바지할 수 있을 것으로 기대된다.