• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1307-1323
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• 2014

With the exhaustion of global IPv4 addresses, IPv6 technologies have attracted increasing attentions, and have been deployed widely. Meanwhile, new applications running over IPv6 networks will change the traditional traffic characteristics obtained from IPv4 networks. Traditional models obtained from IPv4 cannot be used for IPv6 network monitoring directly and there is a need to investigate those changes. In this paper, we explore the flow features of IPv6 traffic and compare its difference with that of IPv4 traffic from flow level. Firstly, we analyze the differences of the general flow statistical characteristics and users' behavior between IPv4 and IPv6 networks. We find that there are more elephant flows in IPv6, which is critical for traffic engineering. Secondly, we find that there exist many one-way flows both in the IPv4 and IPv6 traffic, which are important information sources for abnormal behavior detection. Finally, in light of the challenges of analyzing massive data of large-scale network monitoring, we propose a group flow model which can greatly reduce the number of flows while capturing the primary traffic features, and perform a comparative measurement analysis of group users' behavior dynamic characteristics. We find there are less sharp changes caused by abnormity compared with IPv4, which shows there are less large-scale malicious activities in IPv6 currently. All the evaluation experiments are carried out based on the traffic traces collected from the Northwest Regional Center of CERNET (China Education and Research Network), and the results reveal the detailed flow characteristics of IPv6, which are useful for traffic management and anomaly detection in IPv6.

• 인터넷정보학회논문지
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• 제21권5호
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• pp.119-127
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• 2020

국내 전기차 (EV: Electric Vehicle) 시장이 성장함에 따라, 빠르게 증가하는 EV 충전 수요에 대응하기 위한 충전설비의 확충이 요구되고 있다. 이와 관련하여, 종합적인 설비 계획을 수립하기 위해서는 미래 시점의 충전 수요량을 예측하고 이를 바탕으로 전력설비 부하에 미치는 영향을 체계적으로 분석하는 것이 필요하다. 본 논문에서는 한국전력공사의 EV 충전 데이터를 이용하여 충전소 단위의 일별최대부하를 예측하는 LSTM(Long Short-Term Memory) 신경망 모델을 설계 및 개발한다. 이를 위해, 먼저 데이터 전처리 및 이상치 제거를 통해 정제된 데이터를 얻는다. 다음으로, 충전소 단위의 일별 특징들을 추출하여 훈련 데이터 집합을 구성하여 일별 최대 전력부하 예측 모델을 학습시킨다. 마지막으로 충전소 유형 별 테스트 집합을 이용한 성능 분석을 통해 예측 모델을 검증하고 이의 한계점을 논의한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Asian Pacific Journal of Cancer Prevention
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• 제15권12호
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• pp.4823-4827
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• 2014

Background: Since inconsistent results have been reported regarding the relation between the matrix metalloproteinase-2 (MMP-2) -1306C>T polymorphism and susceptibility for breast cancer, we performed a meta-analysis to investigate the issue. Materials and Methods: An internet search of PubMed and EMBASE was performed to identify eligible studies. Pooled odds ratios (ORs) with their corresponding confidence intervals (CIs) were calculated to evaluate any association between MMP-2 -1306C>T polymorphism and breast cancer susceptibility. Results: Nine case-control studies were included in the meta-analysis, involving 9,858 cases and 10,871 controls. Overall, there was no evidence of any association between the MMP-2 -1306C>T polymorphism and breast cancer susceptibility in different genetic models (T-allele vs C-allele: OR=0.95, 95%CI, 0.82-1.10, p=0.49; TT vs CC: OR=1.03, 95%CI, 0.90-1.19, p=0.66; TT+TC vs CC: OR=0.93, 95%CI, 0.78-1.10, p=0.38; TT vs TC+CC: OR=1.02, 95%CI, 0.89-1.17, p=0.77). In the subgroup analysis by ethnicity, CC was associated with a significant increase in breast susceptibility among Latin-Americans in the dominant model (OR=0.61, 95%CI, 0.40-0.93, p=0.02), but the association disappeared in other models. No significant association was observed among Europeans, East Asians and others in different genetic models. In the subgroup analysis by their source of controls, no significant association between MMP-2 -1306C>T polymorphism and breast cancer susceptibility was noted among population-based studies and hospital-based studies in different genetic models. Conclusions: The results of this meta-analysis suggest that MMP-2 -1306C>T polymorphism is not associated with breast cancer susceptibility, although the association among Latin-Americans in the dominant model was significant.

• 사물인터넷융복합논문지
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• 제9권2호
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• pp.71-76
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• 2023

본 논문에서는 단일 수신기가 사용되는 환경에서 정확한 거리 추정을 위해 심층 인공신경망 (Deep Neural Network, DNN)을 활용한 Smoothed Received Signal Strength Indicator (RSSI) 기반 거리 추정 기법을 제안한다. 제안 기법은 거리 추정 정확도 향상을 위해 Data Splitting, 결측치 대치, Smoothing 단계로 구성된 전처리 과정을 수행하여 Smoothed RSSI 값을 도출한다. 도출된 다수의 Smoothed RSSI 값은 Multi-Input Single-Output(MISO) DNN 모델의 Input Data로 사용되며 Input Layer와 Hidden Layer를 통과하여 최종적으로 Output Layer에서 추정 거리로 반환된다. 제안 기법의 우수성을 입증하기 위해 제안 기법과 선형회귀 기반 거리 추정 기법의 성능을 비교하였다. 실험 결과, 제안 기법이 선형회귀 기반 거리 추정 기법 대비 29.09% 더 높은 거리 추정 정확도를 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권3호
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• pp.1040-1057
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• 2014

In this paper, a new bit rate estimation scheme is proposed to determine the bit rate for each subclass in an MPEG-2 TS to H.264/AVC transcoder after dividing an input MPEG-2 TS sequence into several subclasses. Video format transcoding in conventional IPTV and Smart TV services is a time-consuming process since the input sequence should be fully transcoded several times with different bit-rates to decide the bit-rate suitable for a service. The proposed scheme can automatically decide the bit-rate for the transcoded video sequence in those services which can be stored on a video streaming server as small as possible without losing any subject quality loss. In the proposed scheme, an input sequence to the transcoder is sub-classified by hierarchical clustering using a parameter value extracted from each frame. The candidate frames of each subclass are used to estimate the bit rate using a statistical analysis and a mathematical model. Experimental results show that the proposed scheme reduces the bit rate by, on an average approximately 52% in low-complexity video and 6% in high-complexity video with negligible degradation in subjective quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1799-1818
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• 2018

Malware detections continue to be a challenging task as attackers may be aware of the rules used in malware detection mechanisms and constantly generate new breeds of malware to evade the current malware detection mechanisms. Consequently, novel and innovated malware detection techniques need to be investigated to deal with this circumstance. In this paper, we propose a new secure malware detection system in which API call fragments are used to recognize potential malware instances, and these API call fragments together with the homomorphic encryption technique are used to construct a privacy-preserving Naive Bayes classifier (PP-NBC). Experimental results demonstrate that the proposed PP-NBC can successfully classify instances of malware with a hit-rate as high as 94.93%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2804-2823
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• 2013

Wireless sensor network (WSN) is a promising technology for monitoring physical phenomena at fine-grained spatial and temporal resolution. However, the typical approach of sending each sensed measurement out of the network for detailed spatial analysis of transient physical phenomena may not be an efficient or scalable solution. This paper focuses on in-network physical phenomena detection schemes, particularly the distributed computation of the boundary of physical phenomena (i.e. event), to support energy efficient spatial analysis in wireless sensor networks. In-network processing approach reduces the amount of network traffic and thus achieves network scalability and lifetime longevity. This study investigates the recent advances in distributed event detection based on in-network processing and includes a concise comparison of various existing schemes. These boundary detection schemes identify not only those sensor nodes that lie on the boundary of the physical phenomena but also the interior nodes. This constitutes an event geometry which is a basic building block of many spatial queries. In this paper, we introduce the challenges and opportunities for research in the field of in-network distributed event geometry boundary detection as well as illustrate the current status of research in this field. We also present new areas where the event geometry boundary detection can be of significant importance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5527-5545
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• 2019

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.