• Journal of Information Processing Systems
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• 제19권5호
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• 한국항행학회논문지
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• 제14권6호
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• pp.996-1001
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• 2010

본 논문에서는 Mobile IPTV의 서비스를 위한 네트워크 선택 알고리즘을 제안하고 처리속도가 낮은 단말기의 부하를 줄이기 위한 계층별 처리 시스템을 구현하였다. 제안한 알고리즘은 새로운 사용자가 네트워크에 접속할 때 잔여 대역폭, 네트워크 비용, 신호세기를 이용하는 계산식에서 나온 우선순위 값으로 네트워크를 선택한다. 처리속도가 낮은 단말기의 응용계층에서 네트워크 선택과 TV서비스 제공을 모두하면 시스템 부하로 서비스의 품질이 낮아지게 되므로 네트워크 선택과 TV 서비스기능을 계층별로 나누어 처리하는 시스템을 구현하였다. 실험결과 사용자가 직접 네트워크를 선택하는 방법에서는 대역폭 할당을 위한 대기시간이 존재하고, 대역폭을 모두 소진한 경우 대역폭 할당을 위한 대기 시간이 길어지는 반면 본 논문에서 제안한 우선순위를 이용한 네트워크 선택 알고리즘을 이용할 경우 여러 네트워크 중 상태가 좋은 네트워크를 선택하기 때문에 데이터 전송속도가 평균적으로 유지가 되고 대역폭 할당 대기시간이 낮아지는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2214-2229
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• 2024

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters.

• 한국통신학회논문지
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• 제32권11C호
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• pp.1066-1072
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• 2007

본 논문에서는 동영상 부호화 방식의 움직임 추정을 위한 고속 다중 참조 프레임 선택 기법에 대해 제안한다. 움직임 추정을 위해 다중 프레임을 참조하는 H.264 동영상 부호화 방식의 경우, 블록별로 선택된 최적의 참조 프레임들은 일정한 상관관계를 유지하는 특성이 존재한다. 제안 방식은 이러한 통계적 특성을 이용하여, 임의의 블록 크기의 움직임 추정 모드에서 선택된 최적의 참조 프레임 정보를 이용하여 하위 블록 크기의 움직임 추정 모드에서 사용할 참조 프레임을 예측한다. 실험 결과를 통해 5개의 참조 프레임 사용을 기준으로 제안하는 기법을 사용하였을 때, 움직임 추정에 소요되는 시간은 가용한 모든 참조 프레임에 대해 움직임 추정을 수행하는 방식 대비 평균 60% 감소되었으며, PSNR 및 발생 비트율 측면에서 거의 동일한 성능을 유지함을 확인할 수 있었다.

• 대한화장품학회:학술대회논문집
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• 대한화장품학회 2003년도 IFSCC Conference Proceeding Book I
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• pp.457-468
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• 2003

Active ingredients have been around in cosmetics for a long time but have they really resulted in active cosmetic products\ulcorner In order to achieve this, the right active needs to be delivered to the right location at the right concentration for the correct period of time. And the extent (and therefore the concentration) of this delivery depends on the formulation. From a rather theoretical approach based on the polarity of the active ingredient, the stratum corneum and the oil phase, the Relative Polarity Index was established that enables the selection of a suitable emollient for ensuring skin penetration of the active ingredient. Practical examples subsequently show the validity of this approach that demonstrates that one can regulate the delivery of an active molecule (and therefore the efficacy of a cosmetic formulation) by selection and control of the emollient system. Cosmetic formulations are generally quite complex mixtures and subsequent experiments using different emulsifier systems indicated that this component of a cosmetic formulation could also have an impact on steering the active ingredient to the right layer of the skin, although it is too early to be able to derive general rules from this.

• 산업경영시스템학회지
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• 제42권3호
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• pp.167-175
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• 2019

The purpose of this study is to analyze the factors influencing consumers purchasing of domestic, imported and craft beer through AHP analysis and to provide implications for marketing for each beer market. In this study, theories and calculations related to AHP analysis were thoroughly examined and selection attributes were determined by referring to existing theories. A total of 164 consumers who have purchased beer were the target of the survey. The results were analyzed by AHP analysis and the differences were analyzed. It was confirmed that the domestic, imported, and craft beer had the highest weight in the taste in the first layer. At this time, we can confirm that imported, and craft beer has a relatively high weight on taste and domestic beer has a relatively high weight on brand image. We also found that design and advertising images do not have a significant impact on beer selection. Even though it is study on beer, we can find people have different preference between their orgin. In this study, it is possible to show what type of factors does the beer manufacturing and distribution company should concentrate on by analyzing factors that consumers consider, unlike other studies focusing on consumption status of existing beer.

• 융합정보논문지
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• 제11권7호
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• pp.1-6
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• 2021

본 연구의 목적은 이미지 랜드마크 선정 기법을 기반으로, 인공신경망 안면 영상분석 시스템의 성능을 향상하기 위한 내용이다. 랜드마크 선정을 위하여 안면 이미지 연령을 분류를 위한 CNN 기반의 다층 ResNet 모델의 구성이 필요하며, ResNet 모델에서 입력 노드의 변화에 따른 출력 노드의 변화를 감지하는 히트 맵을 추출한다. 추출된 다수의 히트 맵을 결합하여 연령 구분 예측과 관계된 안면 랜드마크를 구성한다. 이를 통하여, 안면 랜드마크를 통하여 픽셀의 위치별 중요도를 분석할 수 있으며, 가중치가 낮은 픽셀의 제거함으로서 상당량의 입력 데이터 감소가 가능해졌다. 이러한 기법은 인공신경망 시스템의 연산 성능 향상에 기여하게 된다.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3204-3217
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• 2023

In this paper, the downlink of the multicast based spatial modulation systems is investigated. Specifically, physical layer multicasting is introduced to increase the number of access users and to improve the communication rate of the spatial modulation system in which only single radio frequency chain is activated in each transmission. To minimize the bit error rate (BER) of the multicast based spatial modulation system, a joint optimizing algorithm of antenna selection and multicast precoding is proposed. Firstly, the joint optimization is transformed into a mixed-integer non-linear program based on single-stage reformulation. Then, a novel iterative algorithm based on the idea of branch and bound is proposed to obtain the quasioptimal solution. Furthermore, in order to balance the performance and time complexity, a low-complexity deflation algorithm based on the successive convex approximation is proposed which can obtain a sub-optimal solution. Finally, numerical results are showed that the convergence of our proposed iterative algorithm is between 10 and 15 iterations and the signal-to-noise-ratio (SNR) of the iterative algorithm is 1-2dB lower than the exhaustive search based algorithm under the same BER accuracy conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2268-2283
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• 2013

Scalable video coding can handle peer heterogeneity of P2P streaming applications, but there is still a lack of comprehensive studies on how to use it to improve video playback quality. In this paper we propose a capacity aware scalable video coding mechanism for P2P on demand streaming system. The proposed mechanism includes capacity based neighbor selection, adaptive data scheduling and streaming layer adjustment, and can enable each peer to select appropriate streaming layers and acquire streaming chunks with proper sequence, along with choosing specific peers to provide them. Simulation results show that the presented mechanism can decrease the system's startup and playback delay, and increase the video playback quality as well as playback continuity, and thus it provides a better quality of experience for users.