• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.1119-1122
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• 2005

기계학습 방법을 이용한 네트워크 기반 침입탐지 시스템은 어떤 학습알고리즘을 사용하여 구현되었느냐에 따라 그 결과가 매우 달라진다. 학습을 위한 전처리를 많이 하면 비례하여 성능이 개선되지만, 실제 사용의 유용성면에서는 성능이 떨어지게 된다. 따라서 최소한의 전처리를 하여 침입탐지의 탐지율을 보장하는 방법이 필요 하다. 본 논문에서는 네트워크기반 침입탐지 문제를 기계학습을 이용하여 해결하는 방법을 제안 하였다. 제안된 모델은 탐지 속도와 각종 공격들의 패킷 분포를 고려하여 관련된 그룹으로 분류하고, 이것을 학습하는 시스템이다. 실험을 통하여 제안된 모델의 유용성을 검증 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.527-529
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• 2014

다양한 컴퓨팅 서비스가 등장하고 진화하면서, 서비스를 제공하기 위한 컴퓨팅 시스템은 점점 복잡해지고 있다. 시스템이 복잡해질수록 다양한 취약점들이 발생하고, 실제로 이 취약점들을 악용한 공격도 점점 늘어나고 있다. 수많은 공격들을 막기 위한 보안 솔루션은 크게 네트워크 보안 솔루션과 호스트 보안 솔루션으로 나뉘며, 그 중 네트워크 보안 솔루션은 misuse detection과 anomaly detection으로 크게 분류된다. 안전한 시스템을 구축하기 위해선 유입되는 공격시도를 최대한 많이 발견하고 차단하는 것이 매우 중요한데, 이러한 관점에서 네트워크 보안은 매우 중요하다. 본 논문에서는 네트워크 보안 솔루션의 보안 성능 강화를 위해 misuse detection과 anomaly detection을 융합하여 철저한 패킷 검사를 수행할 수 있는 아키텍처를 제안한다. 그리고 제시한 아키텍처의 한계점을 기술하고 이 문제를 SDN을 활용하여 해결하는 방안도 제시한다.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.105-121
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• 1999

Computer security is considered important due to the side effect generated from the expansion of computer network and rapid increase of the use of computers. Intrusion Detection System(IDS) has been an active research area to reduce the risk from intruders. We propose a new IDS model, which consists of several computers with IDS, based on the immune system model and describe the design of the IDS model and the prototype implementation of it for feasibility testing and evaluate the performance of the IDS in the aspect of detection time, detection accuracy, diversity which is feature of immune system, and system overhead. The IDSs are distributed and if any of distributed IDSs detect anomaly system call among system call sequences generated by a privilege process, the anomaly system call can be dynamically shared with other IDSs. This makes the IDSs improve the ability of immunity for new intruders.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1030-1036
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• 2006

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function considering weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the method that assigns different weighted values to feature attributes depending on importance.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.451-456
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• 2004

Signature based intrusion detection system (IDS), having stored rules for detecting intrusions at the library, judges whether new inputs are intrusion or not by matching them with the new inputs. However their policy has two restrictions generally. First, when they couldn't make rules against new intrusions, false negative (FN) errors may are taken place. Second, when they made a lot of rules for maintaining diversification, the amount of resources grows larger proportional to their amount. In this paper, we propose the learning algorithm which can evolve the competent of anomaly detectors having the ability to detect anomalous attacks by genetic algorithm. The anomaly detectors are the population be composed of by following the negative selection procedure of the biological immune system. To show the effectiveness of proposed system, we apply the learning algorithm to the artificial network environment, which is a computer security system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.2
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• pp.115-125
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• 2003

As Web services are generally open for external uses and not filtered by Firewall, these result in attacker's target. Web attacks which exploit vulnerable web-applications and malicious users' requests cause economical and social problems. In this paper, we are modelling general web service usages based on user-web-session and detect anomal usages with Bayesian estimation method. Finally we propose SAD(Session Anomaly Detection) for detection unknown web attacks. To evaluate SAD, we made an experiment on attack simulation with web vulnerability scanner, whisker. The results show that the detection rate of SAD is over 90%, which is influenced by several features such as size of window or training set, detection filter method and web topology.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.157-163
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• 2022

Recently, with the development of automation in the industrial field, research on anomaly detection is being actively conducted. An application for anomaly detection used in factory automation is camera-based defect inspection. Vision camera inspection shows high performance and efficiency in factory automation, but it is difficult to overcome the instability of lighting and environmental conditions. Although camera inspection using deep learning can solve the problem of vision camera inspection with much higher performance, it is difficult to apply to actual industrial fields because it requires a huge amount of normal and abnormal data for learning. Therefore, in this study, we propose a network that overcomes the problem of collecting abnormal data with 72 geometric transformation deep learning methods using only normal data and adds an outlier exposure method for performance improvement. By applying and verifying this to the MVTec data set, which is a database for auto-mobile parts data and outlier detection, it is shown that it can be applied in actual industrial sites.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.453-458
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• 2024

Due to global issues such as climate crisis and rising energy costs, there is an increasing focus on energy conservation and management. In the case of South Korea, approximately 53.5% of the total energy consumption comes from industrial complexes. In order to address this, we aimed to improve issues through the 'Shared Network Utility Plant' among companies using similar energy utilities to find energy-saving points. For effective energy conservation, various techniques are utilized, and stable data supply is crucial for the reliable operation of factories. Many anomaly detection and alert systems for checking the stability of data supply were dependent on Energy Management Systems (EMS), which had limitations. The construction of an EMS involves large-scale systems, making it difficult to implement in small factories with spatial and energy constraints. In this paper, we aim to overcome these challenges by constructing a data collection system and anomaly detection alert system on embedded devices that consume minimal space and power. We explore the possibilities of utilizing anomaly detection alert systems in typical institutions for data collection and study the construction process.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.219-222
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• 2006

In this paper an intrusion detection system technique of wireless Ad Hoc network is explained and the advantage of making them work in IEEE 802.15.4/ZigBee wireless standard is also discussed. The methodology that is mentioned here is intrusion detection architecture based on a local intrusion database [1]. An ad hoc network is a collection of nodes that is connected through a wireless medium forming rapidly changing topologies. Due to increased connectivity (especially on the Internet), and the vast spectrum of financial possibilities that are opening up, more and more systems are subject to attack by intruders. An ideal IDS should able to detect an anomaly caused by the intruders quickly so that the misbehaving node/nodes can be identified and appropriate actions (e.g. punish or avoid misbehaving nodes) can be taken so that further damage to the network is minimized