• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.159-167
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• 2023

Objective: • To detect black hole and warm hole attacks in wireless sensor networks. • To give a solution for energy depletion and security breach in wireless sensor networks. • To address the security problem using strategic decision support system. Methods: The proposed stackelberg game is used to make the spirited relations between multi leaders and multi followers. In this game, all cluster heads are acts as leaders, whereas agent nodes are acts as followers. The game is initially modeled as Quadratic Programming and also use backtracking search optimization algorithm for getting threshold value to determine the optimal strategies of both defender and attacker. Findings: To find optimal payoffs of multi leaders and multi followers are based on their utility functions. The attacks are easily detected based on some defined rules and optimum results of the game. Finally, the simulations are executed in matlab and the impacts of detection of black hole and warm hole attacks are also presented in this paper. Novelty: The novelty of this study is to considering the stackelberg game with backtracking search optimization algorithm (BSOA). BSOA is based on iterative process which tries to minimize the objective function. Thus we obtain the better optimization results than the earlier approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권7호
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• pp.1721-1736
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• 2013

Black and gray hole attack is one kind of routing disturbing attacks and can bring great damage to the network. As a result, an efficient algorithm to detect black and gray attack is important. This paper demonstrate an adaptive approach to detecting black and gray hole attacks in ad hoc network based on a cross layer design. In network layer, we proposed a path-based method to overhear the next hop's action. This scheme does not send out extra control packets and saves the system resources of the detecting node. In MAC layer, a collision rate reporting system is established to estimate dynamic detecting threshold so as to lower the false positive rate under high network overload. We choose DSR protocol to test our algorithm and ns-2 as our simulation tool. Our experiment result verifies our theory: the average detection rate is above 90% and the false positive rate is below 10%. Moreover, the adaptive threshold strategy contributes to decrease the false positive rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5574-5591
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• 2017

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

• International Journal of Computer Science & Network Security
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• 제24권8호
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• pp.139-144
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• 2024

VANETs have become one of the most attractive research areas in the world of wireless networks in recent years. Indeed, vehicular networks have become capable of optimizing road traffic, which significantly reduces the number of accidents through notifications exchanged between nearby vehicles. The routing function based on the opportunistic algorithm is a critical part of the vehicle's communication system and will therefore be an ideal target for attacks that could aim to prevent alert messages from reaching their destination, and thus endanger human lives. The black hole attack is a major threat to the security of VANETs. The main idea of this paper focuses on the analysis of this type of attack in VANETs using Discrete-Time Markov Chains (DTMC). and deduce at the end the effect of the number of malicious nodes on the delivery rate in the network.

• Smart Structures and Systems
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• 제29권3호
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• pp.513-522
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• 2022

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.

• 천문학회보
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• 제45권1호
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• pp.51.3-52
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• 2020

We present the status of the development of a Markov Chain Monte Carlo (MCMC) parameter estimation (PE) pipeline for compact binary coalescences (CBCs) with the Japanese KAGRA gravitational-wave (GW) detector. The pipeline is included in the KAGRA Algorithm Library (KAGALI). Basic functionalities are benchmarked from the LIGO Algorithm Library (LALSuite) but the KAGRA MCMC PE pipeline will provide a simpler, memory-efficient pipeline to estimate physical parameters from gravitational waves emitted from compact binaries consisting of black holes or neutron stars. Applying inspiral-merge-ringdown and inspiral waveforms, we performed simulations of various black hole binaries, we performed the code sanity check and performance test. In this talk, we present the situation of GW observation with the Covid-19 pandemic. In addition to preliminary PE results with the KAGALI MCMC PE pipeline, we discuss how we can optimize a CBC PE pipeline toward the next observation run.

• 천문학회보
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• 제41권2호
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• pp.61.1-61.1
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• 2016

Black hole (BH) mass is a fundamental quantity to understand BH growth, galaxy evolution, and connection between them. Thus, obtaining accurate and precise BH mass estimates over cosmic time is of paramount importance. The rest-frame UV CIV ${\lambda}1549$ broad emission line is commonly used for BH mass estimates in high-redshift AGNs (i.e., $2{\leq}z{\leq}5$) when single-epoch (SE) optical spectra are available. Achieving correct and accurate calibration for CIV-based SE BH mass estimators against the most reliable reverberation-mapping based BH mass estimates is thus practically important and still useful. By performing multi-component spectral decomposition analysis to obtained high-quality HST UV spectra for the updated sample of local reverberation-mapped AGNs including new HST STIS observations, CIV emission line widths and continuum luminosities are consistently measured. Using a Bayesian hierarchical model with MCMC sampling based on Hamiltonian Monte Carlo algorithm (Stan NUTS), we provide the most consistent and accurate calibration of CIV-based BH mass estimators for the three line width characterizations, i.e., full width at half maximum (FWHM), line dispersion (${\sigma}_{line}$), and mean absolute deviation (MAD), in the extended BH mass dynamic range of log $M_{BH}/M_{\odot}=6.5-9.1$.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.91-97
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• 2000

In this paper, the illuminator is designed to recognize the shape and the existence of holes of radiation fin in the point that the light reflection characteristics are different according to the roughness of the material. The threshold value, the positions of holes and the black pixel nembers in the positon are obtained under the illuminator, in accordance with the reference image, by applying binary conversion and hole segmentation algorithm, as they are suggested in this paper, The existence and shape of hole are recognized by calculating the distance and feature value in the test image, which is obtained from the parameters of reference image. It is programmed to apply to GUI(Graphic User the Interface) in windows. More than 98% of recognition rate is shown, as it is applied to three different sizes of the radiation fin.

• 한국산학기술학회논문지
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• 제17권9호
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• pp.655-662
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• 2016

의료용 CT영상에서 영상의 화질 저하의 원인으로 인공물을 유발의 원인인 다양한 금속성분의 의료용 재료를 이용하여, 듀얼에너지 CT의 MAR 알고리즘적용 전, 후 영상의 CT value값을 비교 측정하고, 정량적 분석을 통해 MAR Algorithm 적용의 유용성을 알아보았다. 그 결과 MAR 알고리즘 적용 시 대부분의 의료용 금속 물질에서 인공물 감소효과가 있었다(P<0.05). 특히, 의료용 금속물질 중 Stainless합금(78.1% 감소)과 백금소재인 GDC coil(76.1% 감소)처럼 상대적으로 밀도가 높은 의료용 금속물질에서 인공물 감소효과가 높았다(P<0.05). 또한, 인공물 형태에 따른 인공물 감소정도를 정량적 분석을 통해 알아보았다. 그 결과는 MAR 알고리즘 적용 시 Stainless합금과 Titanium합금의 경우 Black hole artifact형태에서 인공물 감소효과가 매우 우수하였고(P<0.05), 백금소재인 GDC coil의 경우는 White streak artifact형태에서 인공물 감소효과가 매우 우수하였다(P<0.05). 향후 의료용 금속 물질 삽입 수술 환자의 경우 듀얼 에너지 CT장비의 MAR알고리즘을 적용하여 CT검사를 시행한다면 금속물질에 기인한 인공물 유발을 감소시켜 보다 뛰어난 화질의 영상정보를 제공할 수 있을 것으로 사료된다.

• Journal of Radiation Protection and Research
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• 제36권2호
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• pp.79-85
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• 2011

본 논고의 목적은 듀얼 에너지 시스템의 금속 인공물 감약 (MAR) 기법을 팬텀과 환자를 대상으로 적용하여보고 그 유용성에 대해서 알아보고자 한다. 사용한 장비는 GE사(社)의 750HD CT이고 AW 4.5 볼륨 share 4 워크스테이션에서 영상 분석을 하였다. 팬텀으로는 victoreen사(社)의 사람 모형 흉부팬텀을 사용하였고 사람 대상은 2010년 12월 20일부터 2011년 2월 10일까지 금속물질이 삽입된 환자 25명을 대상으로 목 부위와 복부 부위에 MAR 알고리즘을 적용하였다. 사용한 파라미터는 복부검사의 경우 GSI-4Body-Large 0.9 sec 40 mm이고 목 부위에서는 GSI-3Body-Medium 0.5 sec 40 mm를 선택하여 시행하였다. 영상시험은 국립암센터 CT영상실에서 기본적으로 시행하고 있는 프로토콜에 MAR 옵션을 적용하여 동일하게 시행하였다. 영상분석은 MAR기법을 적용한 영상과 적용하지 않은 영상에서 검은색 인공물과 줄무늬 인공물이 있는 부위에 관심부위 (ROI)를 설정한 후 hounsfield unit(HU)과 노이즈를 이용한 정량적 평가를 하였고 임상 경험이 풍부한 의사와 방사선사가 CT영상에 대한 평가를 하여 MAR 알고리즘에 대한 유용성을 비교하였다. 환자 대상 시험 결과 MAR 알고리즘을 적용함으로서 금속 인공물이 있는 부위에서의 검은색 인공물(금속이 있는 부위) 과 줄무늬 인공물이 있는 영상에서 HU의 p값이 각각 p=0.01과 p=0.04를 보였지만 금속 인공물의 영향에서 벗어난 부위에서의 노이즈는 오히려 증가하는 모습을 보였다(p=0.05). 그리고 그 외의 영상(금속이 있는 부위에서 1 cm 떨어진 단면 영상) 에서 검은색 인공물과 줄무늬 인공물 부위의 HU와 노이즈의 p값에 대해서는 통계적 유의성은 만족하지 못하였지만 대체적으로 MAR알고리즘을 적용함으로서 금속인공물 발생이 감약 되는 결과를 보였다. 결론적으로 금속 물질이 삽입된 환자의 경우에 MAR 알고리즘을 적용함으로서 조직의 선예도가 적용하지 안했을 때보다 증가하였고 검은색 인공물 발생 또한 개선되는 모습을 보였다.