• Title/Summary/Keyword: Rate of Learning

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Sleep Deprivation Attack Detection Based on Clustering in Wireless Sensor Network (무선 센서 네트워크에서 클러스터링 기반 Sleep Deprivation Attack 탐지 모델)

  • Kim, Suk-young;Moon, Jong-sub
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.31 no.1
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    • pp.83-97
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    • 2021
  • Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

A Study on the stock price prediction and influence factors through NARX neural network optimization (NARX 신경망 최적화를 통한 주가 예측 및 영향 요인에 관한 연구)

  • Cheon, Min Jong;Lee, Ook
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.8
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    • pp.572-578
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    • 2020
  • The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.

Who Gets Government SME R&D Subsidy? Application of Gradient Boosting Model (Gradient Boosting 모형을 이용한 중소기업 R&D 지원금 결정요인 분석)

  • Kang, Sung Won;Kang, HeeChan
    • The Journal of Society for e-Business Studies
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    • v.25 no.4
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    • pp.77-109
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    • 2020
  • In this paper, we build a gradient Boosting model to predict government SME R&D subsidy, select features of high importance, and measure the impact of each features to the predicted subsidy using PDP and SHAP value. Unlike previous empirical researches, we focus on the effect of the R&D subsidy distribution pattern to the incentive of the firms participating subsidy competition. We used the firm data constructed by KISTEP linking government R&D subsidy record with financial statements provided by NICE, and applied a Gradient Boosting model to predict R&D subsidy. We found that firms with higher R&D performance and larger R&D investment tend to have higher R&D subsidies, but firms with higher operation profit or total asset turnover rate tend to have lower R&D subsidies. Our results suggest that current government R&D subsidy distribution pattern provides incentive to improve R&D project performance, but not business performance.

A Study on the Cognitive Judgment of Pedestrian Risk Factors Using a Second-hand Mobile Phones (중고스마트폰 업사이클링을 통한 보행위험요인 인지판단 연구)

  • Chang, IlJoon;Jeong, Jongmo;Lee, Jaeduk;Ahn, Se-young
    • The Journal of the Korea Contents Association
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    • v.22 no.1
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    • pp.274-282
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    • 2022
  • In order to secure pedestrians' right to walk, we have up-cycled second hand mobile phones to overcome limitations of the existing survey methods, analysis methods, and diagnosis to reduce pedestrian traffic accidents. Second hand mobile phones were up-cycled to produce mobile CCTVs and installed in areas where pedestrian deaths rate is high to secure image data sets for the period of more than 24 hours. It was analyzed by applying image visualization technology and clouding reporting technology, and more precise and accurate results were derived through modeling based on artificial intelligence learning and GIS-based diagnostic guidance. As a result, it was possible to analyze the risk factors and number of pedestrian safety, and even factors that were not known in the existing method could be derived. In addition, the traffic accident risk index was derived by converting data into one year to verify whether second hand mobile phone up-cycling mobile CCTV will be an objective tool for finding pedestrian risk factors. Up-cycling mobile CCTV of second hand mobile phones newly applied through research can be used as a new tool to find pedestrian risk factors, and it can be used as a service to protect the safety of the traffic vulnerable other than pedestrians.

Traffic Flooding Attack Detection on SNMP MIB Using SVM (SVM을 이용한 SNMP MIB에서의 트래픽 폭주 공격 탐지)

  • Yu, Jae-Hak;Park, Jun-Sang;Lee, Han-Sung;Kim, Myung-Sup;Park, Dai-Hee
    • The KIPS Transactions:PartC
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    • v.15C no.5
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    • pp.351-358
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    • 2008
  • Recently, as network flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems(IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network environment. In this paper we propose a lightweight and fast detection mechanism for traffic flooding attacks. Firstly, we use SNMP MIB statistical data gathered from SNMP agents, instead of raw packet data from network links. Secondly, we use a machine learning approach based on a Support Vector Machine(SVM) for attack classification. Using MIB and SVM, we achieved fast detection with high accuracy, the minimization of the system burden, and extendibility for system deployment. The proposed mechanism is constructed in a hierarchical structure, which first distinguishes attack traffic from normal traffic and then determines the type of attacks in detail. Using MIB data sets collected from real experiments involving a DDoS attack, we validate the possibility of our approaches. It is shown that network attacks are detected with high efficiency, and classified with low false alarms.

Causal inference from nonrandomized data: key concepts and recent trends (비실험 자료로부터의 인과 추론: 핵심 개념과 최근 동향)

  • Choi, Young-Geun;Yu, Donghyeon
    • The Korean Journal of Applied Statistics
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    • v.32 no.2
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    • pp.173-185
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    • 2019
  • Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

Hybrid Offloading Technique Based on Auction Theory and Reinforcement Learning in MEC Industrial IoT Environment (MEC 산업용 IoT 환경에서 경매 이론과 강화 학습 기반의 하이브리드 오프로딩 기법)

  • Bae Hyeon Ji;Kim Sung Wook
    • KIPS Transactions on Computer and Communication Systems
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    • v.12 no.9
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    • pp.263-272
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    • 2023
  • Industrial Internet of Things (IIoT) is an important factor in increasing production efficiency in industrial sectors, along with data collection, exchange and analysis through large-scale connectivity. However, as traffic increases explosively due to the recent spread of IIoT, an allocation method that can efficiently process traffic is required. In this thesis, I propose a two-stage task offloading decision method to increase successful task throughput in an IIoT environment. In addition, I consider a hybrid offloading system that can offload compute-intensive tasks to a mobile edge computing server via a cellular link or to a nearby IIoT device via a Device to Device (D2D) link. The first stage is to design an incentive mechanism to prevent devices participating in task offloading from acting selfishly and giving difficulties in improving task throughput. Among the mechanism design, McAfee's mechanism is used to control the selfish behavior of the devices that process the task and to increase the overall system throughput. After that, in stage 2, I propose a multi-armed bandit (MAB)-based task offloading decision method in a non-stationary environment by considering the irregular movement of the IIoT device. Experimental results show that the proposed method can obtain better performance in terms of overall system throughput, communication failure rate and regret compared to other existing methods.

A TBM data-based ground prediction using deep neural network (심층 신경망을 이용한 TBM 데이터 기반의 굴착 지반 예측 연구)

  • Kim, Tae-Hwan;Kwak, No-Sang;Kim, Taek Kon;Jung, Sabum;Ko, Tae Young
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.23 no.1
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    • pp.13-24
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    • 2021
  • Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

Implementation of reliable dynamic honeypot file creation system for ransomware attack detection (랜섬웨어 공격탐지를 위한 신뢰성 있는 동적 허니팟 파일 생성 시스템 구현)

  • Kyoung Wan Kug;Yeon Seung Ryu;Sam Beom Shin
    • Convergence Security Journal
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    • v.23 no.2
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    • pp.27-36
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    • 2023
  • In recent years, ransomware attacks have become more organized and specialized, with the sophistication of attacks targeting specific individuals or organizations using tactics such as social engineering, spear phishing, and even machine learning, some operating as business models. In order to effectively respond to this, various researches and solutions are being developed and operated to detect and prevent attacks before they cause serious damage. In particular, honeypots can be used to minimize the risk of attack on IT systems and networks, as well as act as an early warning and advanced security monitoring tool, but in cases where ransomware does not have priority access to the decoy file, or bypasses it completely. has a disadvantage that effective ransomware response is limited. In this paper, this honeypot is optimized for the user environment to create a reliable real-time dynamic honeypot file, minimizing the possibility of an attacker bypassing the honeypot, and increasing the detection rate by preventing the attacker from recognizing that it is a honeypot file. To this end, four models, including a basic data collection model for dynamic honeypot generation, were designed (basic data collection model / user-defined model / sample statistical model / experience accumulation model), and their validity was verified.

Prenatal Diagnosis of the 22q11.2 Duplication Syndrome

  • Lee, Moon-Hee;Park, So-Yeon;Lee, Bom-Yi;Choi, Eun-Young;Kim, Jin-Woo;Park, Ju-Yeon;Lee, Yeon-Woo;Oh, Ah-Rum;Lee, Shin-Young;Yang, Jae-Hyug;Ryu, Hyun-Mee
    • Journal of Genetic Medicine
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    • v.6 no.2
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    • pp.175-178
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    • 2009
  • The 22q11.2 duplication syndrome is an extremely variable disorder with a phenotype ranging from normal to congenital defects and learning disabilities. Recently, the detection rate of 22q11.2 duplication has been increased by molecular techniques, such as array CGH. In this study, we report a familial case of 22q11.2 duplication detected prenatally. Her first pregnancy was terminated because of 22q11.2 duplication detected incidentally by BAC array CGH. The case was referred due to second pregnancy with same 22q11.2 duplication. We perfomed repeat amniocentesis for karyotype and FISH analysis. Karyotype analysis from amniocytes and parental lymphocytes were normal, while FISH analysis of interphase cells presented a duplication of 22q11.2 in the fetus and phenotypically normal mother. The fetal ultrasound showed grossly normal finding. After genetic counseling about variable phenotype with intrafamilial variability with 50% recurrence rate, the couple decided to continue the pregnancy. The newborn had no apparent congenital abnormalities until 2 weeks after birth. We recommend that family members of patients with a 22q11.2 duplication be tested by the interphase FISH analysis. Also, we point out the importance of genetic counseling and an evaluation of the clinical relevance of diagnostic test results.

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