• Nuclear Engineering and Technology
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• 제52권12호
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.207-208
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• 2006

Intrusion detection technology is highlighted in order to establish a safe information-oriented environment. Intrusion detection system can be categorized into anomaly detection and misuse detection according to intrusion detection pattern. In this paper, we propose an architecture to make up for the defect of conventional anomaly intrusion detection. This architecture reduces additional resource consumption and cost by placing the agent in the strategic location in Internet.

• ETRI Journal
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• 제37권3호
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• pp.502-511
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• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

• 디지털산업정보학회논문지
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• 제6권4호
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• pp.153-160
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• 2010

MANET composed mobile nodes without central concentration control like base station communicate through multi-hop route among nodes. Accordingly, it is hard to maintain stability of network because topology of network change at any time owing to movement of mobile nodes. MANET has security problems because of node mobility and needs intrusion detection system that can detect attack of malicious nodes. Therefore, system is protected from malicious attack of intruder in this environment and it has to correspond to attack immediately. In this paper, we propose intrusion detection system based on rules in order to more accurate intrusion detection. Cluster head perform role of monitor node to raise monitor efficiency of packet. In order to evaluate performance of proposed method, we used jamming attack, selective forwarding attack, repetition attack.

• 한국컴퓨터정보학회논문지
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• 제9권1호
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• pp.37-43
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• 2004

컴퓨터 네트워크의 확대 및 인터넷 이용의 급속한 증가에 따라 컴퓨터 보안문제가 중요하게 되었다 따라서 침입자들로부터 위험을 줄이기 위해 침입탐지 시스템에 관한 연구가 진행되고 있다. 본 논문에서는 네트워크 기반의 이상 침입 탐지를 위하여 뉴로-퍼지 기법을 적용하고자 한다 불확실성을 처리하는 퍼지 이론을 이상 침입 탐지영역에 도입하여 적용함으로써 오용 탐지의 한계성을 극복하여 알려지지 않은 침입탐지를 하고자 한다.

• 정보보호학회논문지
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• 제7권4호
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• pp.11-21
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• 1997

This paper introduces a statistical approach of intrusion detection and tunes an intrusion detection model using fuzzy ste. We describel the method of applying fuzzy set for NIDES intensity measure. By using fuzzy set, we improve the algorithm for evaluating score value of NIDES, and present a possibility of intrusion detection system.

• 정보처리학회논문지C
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• 제10C권6호
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• pp.705-710
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• 2003

최근 대부분의 정보 교류가 네트워크 환경 기반에서 이루어지고 있다. 때문에 외부의 침입으로부터 시스템을 보호해 주는 네트워크 침입 탐지 기술에 대한 연구가 매우 중요한 문제로 대두되고 있다. 하지만 시스템에 대한 침입 기술은 날로 새로워지고 더욱 정교화 되고 있어 이에 대한 대비가 절실한 실정이다. 현재 대부분의 침입 탐지 시스템은 이미 알려진 외부의 침입으로부터의 경험 데이터를 이용하여 침입 유형에 효과적으로 대처하지 못하게 된다. 따라서, 본 논문에서는 통계적 학습 이론과 우도비검정 통계량을 이용하여 새로운 침입 유형까지 탐지해 낼 수 있는 변형된 통계적 학습 모형을 제안하였다. 즉, 기존의 정상적인 네트워크 사용에서 벗어나는 형태들에 대한 모형화를 통하여 시스템에 대한 침입 탐지를 수행하였다. KDD Cup-99 Task 데이터를 이용하여 정상적인 네트워크 사용을 벗어나는 새로운 침입을 제안 모형이 효과적으로 탐지함을 확인하였다.

• 한국정보처리학회논문지
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• 제6권5호
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• pp.1332-1341
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• 1999

Rapid expansion of network and increment of computer system access cause computer security to be an important issue. Hence, the researches in intrusion detection system(IDS)are active to reduce the risk from hackers. Considering IDS, we propose a new IDS model(DABIDS : Distributed Agent Based Intelligent intrusion Detection System) based on distributed intrusion detecting agents. The DABIDS dynamically collects intrusion behavior knowledge from each agents when some doubtable behaviors of users are detected and make new agents codes using intrusion scenario data base, and broadcast the detector codes to the distributed intrusion detecting agent of all node. This DABIDS can efficiently solve the problem to reduce the overhead for training detecting agent for intrusion behavior patterns.

• 정보와 통신
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• 제24권11호
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• pp.25-36
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• 2007

Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious attacks over the network. Fundamentally, almost intrusion detection systems have the ability to search through packets and identify contents that match with known attach. In this paper we survey the deep packet inspection implementations techniques, research challenges and algorithm. Finally, we provide a comparison between the different applied system.

• 정보보호학회논문지
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• 제14권2호
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• pp.113-121
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• 2004

인터넷이 급속하게 발전함에 따라 침입탐지의 유형도 매우 다양해지고 복잡해졌다. 이로 인해 발생하는 피해를 막기 위해 많은 침입탐지시스템이 개발되었다. 본 논문에서는 침입탐지시스템 평가기준에 대해 퍼지적분을 이용한 평가방법을 제시하였다.