• Korean Journal of Biological Psychiatry
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• v.22 no.2
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• pp.78-86
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• 2015

Objectives Although ginseng has been reported to protect neuronal cells and improve various cognitive functions, relationship between ginseng supplementation and response inhibition, one of the important cognitive domains has not been explored. In addition, effects of ginseng on in vivo human brain have not been investigated using the diffusion tensor imaging (DTI). The purpose of the current study is to investigate changes in intrusion errors and white matter microstructure after Korean Red Ginseng supplementation using standardized neuropsychological tests and DTI. Methods Fifty-one healthy participants were randomly allocated to the Korean Red Ginseng (n = 26) or placebo (n = 25) groups for 8 weeks. The California Verbal Learning Test was used to assess the number of intrusion errors. Intelligence quotient (IQ) was measured with the Korean Wechsler Adult Intelligence Scale. Depressive and anxiety symptoms were evaluated using Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Hopkins Symptom Checklist-25. The fractional anisotropy (FA) was measured from the brain DTI data. Results After the 8-week intervention, Korean Red Ginseng supplementation significantly reduced intrusion errors after adjusting age, sex, IQ, and baseline score of the intrusion errors (p for interaction = 0.005). Change in FA values in the left anterior corona radiata was greater in the Korean Red Ginseng group compared to the placebo group (t = 4.29, p = 0.04). Conclusions Korean Red Ginseng supplementation may be efficacious for improving response inhibition and white matter microstructure integrity in the prefrontal cortex.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.157-164
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• 2023

The estuary dam is a structure installed and operated in a closed state except when flood event occurs to prevent inland saltwater intrusion and secure freshwater supply. However, the closed state of dam leads to issues such as eutrophication, so it is necessary to examine the extent of saltwater intrusion resulting from the opening of sluice gates. Groundwater, due to its subsurface conditions and slow flow characteristics, is widely analyzed using numerical models. OpenGeoSys, an open-source software capable of simulating Thermal- Hydraulic- Mechanical- Chemical phenomena, was adopted for this study. Simulations were conducted assuming natural flow conditions without dam and operating considering busy farming season, mostly from March to September. Verification of the model through analytical solutions showed error of 3.7%, confirming that OpenGeoSys is capable of simulating saltwater intrusion for these cases. From results simulated for 10 years, considering for the busy farming season, resulted in about 46% reduction in saltwater intrusion length compared to natural flow conditions, approximately 74.36 m. It may be helpful to make choices to use groundwater as a water resource.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.661-663
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• 2001

침입탐지 시스템은 내부자의 불법적인 사용, 오용 또는 외부 침입자에 의한 중요 정보 유출 및 변경을 알아내는 것으로서 각 운영체제에서 사용자가 발생시킨 키워드, 시스템 호출, 시스템 로그, 사용시간, 네트워크 패킷 등의 분석을 통하여 침입여부를 결정한다. 본 논문에서 제안하는 침입탐지시스템은 권한 이동 관련 이벤트 추출 기법을 이용하여 사용자의 권한이 바뀌는 일정한 시점만큼 기록을 한 후 HMM모델에 적용시켜 평가한다. 기존 실험에서 보여주었던 데이터의 신뢰에 대한 단점을 보완하기 위해 다량의 정상행위 데이터와 많은 종류의 침입유형을 적용해 보았고, 그 밖에 몇 가지 단점들을 수정하여 기존 모델에 비해 향상된 성능을 보이는지를 평가하였다 실험 결과 호스트기반의 침입에 대해서 매우 좋은 탐지율을 보여 주었고 F-P error(false positive error) 또한 매우 낮은 수치를 보여 주었다.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.5
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• pp.499-507
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• 2004

We have studied tile detection techniques, which can determine the location and the weight of an intruder into infrastructure, by using fiber-optic ROTDR (Rayleigh optical time domain reflectometry) sensor and fiber-optic BOTDA (Brillouin Optical time domain analysis) sensor, which can use an optical fiber longer than that of ROTDR sensor Fiber-optic sensing plates of ROTDR sensor, which arc buried in sand, were prepared to respond the intruder effects. The signal of ROTDR was analyzed to confirm the detection performance. The constructed ROTDR could be used up to 10km at the pulse width of 30ns. The location error was less than 2 m and the weight could be detected as 4 grades, such as 20kgf, 40kgf, 60kgf and 80kgf. Also, fiber optic BOTDA sensor was developed to be able to detect intrusion effect through an optical fiber of tells of kilometers longer than ROTDR sensor. fiber-optic BOTDA sensor was constructed with 1 laser diode and 2 electro-optic modulators. The intrusion detection experiment was peformed by the strain inducing set-up installed on an optical table to simulate all intrusion effect. In the result of this experiment, the intrusion effort was well detected as the distance resolution of 3m through the fiber length of about 4.81km during 1.5 seconds.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.794-798
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• 2007

As the development of information technology and thus the growth of security incidents, so implement are coming out for defense the intrusion about the system. However the error detection program has got a difficulty to find out the intrusions because that has become so many false alert messages. In this study is how to reduce the messages for the false alerts which come from the internal of the network and using the Fuzzy techniques for reduce the uncertainty of the judge. Therefore it makes the model which can decrease false alert message for better detection.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.674-684
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• 2002

Anomaly detection techniques have teen devised to address the limitations of misuse detection approach for intrusion detection. An HMM is a useful tool to model sequence information whose generation mechanism is not observable and is an optimal modeling technique to minimize false-positive error and to maximize detection rate, However, HMM has the short-coming of login training time. This paper proposes an effective HMM-based IDS that improves the modeling time and performance by only considering the events of privilege flows based on the domain knowledge of attacks. Experimental results show that training with the proposed method is significantly faster than the conventional method trained with all data, as well as no loss of recognition performance.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.410-413
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• 2008

Existing Outlier detections have been widely studied in geosensor networks. Recently, machine learning and data mining have been applied the outlier detection method to build a model that distinguishes outliers based on anchored criterion. However, it is difficult for the existing methods to detect outliers against incoming time-variant data, because outlier detection needs to monitor incoming data and classify irregular attacks. Therefore, in order to solve the problem, we propose a time-variant outlier detection using 2-dimensional grid method based on unanchored criterion. In the paper, outliers using geosensor data was performed to classify efficiently. The proposed method can be utilized applications such as network intrusion detection, stock market analysis, and error data detection in bank account.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.315-329
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• 2001

외부침입탐지 시스템(IDS: Intrusion Detection System)은 컴퓨터의 외부 침입을 자동으로 탐지하는 시스템이다. IDS의 주요목표는 외부사용자들이나 내부 사용자들에서 권한이 없는 사용자, 컴퓨터 오용(misuse) 혹은 잘못된 사용(abuse)을 탐지하는 것이다. 파이어 월(Firewall)이나 암호화와 같은 침입 방지 시스템에 관한 연구와 병행하여 최근 IDS에 대한 다양한 연구가 이루어지고 있다. 침입탐지와 바이러스 탐지에 대한 새로운 접근 방법으로서 면역학적 방법이 동원되고 있다. 이 연구에서는 인간의 인체면역 시스템으로부터 얻어진 몇 가지 주요한 Feature들을 외부침입 탐지에 적용하여 기존의 침입탐지 방법에서 오는 한계점을 극복하여 경고 오류(alarm error rate)를 줄이고자 한다. 따라서 본 연구에서는 외부침입을 탐지하고 시스템을 치유하는 인간의 인체 면역에 대해 기초적인 연구를 진행하였으며 이러한 인체면역 기저들을 네트워크 환경에서 어떻게 실제적으로 적용할 것인 지를 연구하였으며 실제 네트워크 데이터를 적용하여 본 연구에서 제안한 모델에 대한 성능을 테스트하였다.

• Journal of Communications and Networks
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• v.11 no.6
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.