ICT 인프라 이상탐지를 위한 조건부 멀티모달 오토인코더에 관한 연구 (A Study of Anomaly Detection for ICT Infrastructure using Conditional Multimodal Autoencoder)
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- 지능정보연구
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- 제27권3호
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- pp.57-73
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- 2021
ICT 인프라의 이상탐지를 통한 유지보수와 장애 예방이 중요해지고 있다. 장애 예방을 위해서 이상탐지에 대한 관심이 높아지고 있으며, 지금까지의 다양한 이상탐지 기법 중 최근 연구들에서는 딥러닝을 활용하고 있으며 오토인코더를 활용한 모델을 제안하고 있다. 이는 오토인코더가 다차원 다변량에 대해서도 효과적으로 처리가 가능하다는 것이다. 한편 학습 시에는 많은 컴퓨터 자원이 소모되지만 추론과정에서는 연산을 빠르게 수행할 수 있어 실시간 스트리밍 서비스가 가능하다. 본 연구에서는 기존 연구들과 달리 오토인코더에 2가지 요소를 가미하여 이상탐지의 성능을 높이고자 하였다. 먼저 다차원 데이터가 가지고 있는 속성별 특징을 최대한 부각하여 활용하기 위해 멀티모달 개념을 적용한 멀티모달 오토인코더를 적용하였다. CPU, Memory, network 등 서로 연관이 있는 지표들을 묶어 5개의 모달로 구성하여 학습 성능을 높이고자 하였다. 또한, 시계열 데이터의 특징을 데이터의 차원을 늘리지 않고 효과적으로 학습하기 위하여 조건부 오토인코더(conditional autoencoder) 구조를 활용하는 조건부 멀티모달 오토인코더(Conditional Multimodal Autoencoder, CMAE)를 제안하였다. 제안한 CAME 모델은 비교 실험을 통해 검증했으며, 기존 연구들에서 많이 활용된 오토인코더와 비교하여 AUC, Accuracy, Precision, Recall, F1-score의 성능 평가를 진행한 결과 유니모달 오토인코더(UAE)와 멀티모달 오토인코더(Multimodal Autoencoder, MAE)의 성능을 상회하는 결과를 얻어 이상탐지에 있어 효과적이라는 것을 확인하였다.
동악산 청류구곡 일원은 아름다운 계류를 따라 와폭(臥瀑)과 담(潭), 소(沼), 대(臺) 등의 암반경관과 성리문화의 전형으로 향유되어온 구곡문화가 실증적으로 대입된 사례로, 1872년 곡성현 지방도에서 "삼남제일암반계류 청류동(三南第一巖盤溪流 淸流洞)"으로 명기된 바와 같이 풍치가 탁월한 승경처임을 보여준다. 경물(景物)과 경구(警句)가 육로와 수로에 쌍으로 설정되어 차별성을 갖는 청류구곡은 일제강점기인 1916년 이전, 정순태와 조병순의 주도로 설정된 것으로 판단되지만 성리학자들은 물론 불교지도자, 독립운동가 등 선현들의 장구처 등이 다수 발견되는 것으로 볼 때 오래전부터 명인(名人)들의 산수탐방과 은일처로 활용된 것으로 추정된다. 도림사계곡의 기반암 암상에 구성된 청류구곡은 산지형 하천으로 총 길이 약 1.2km, 평균 곡거리 149m로 국내 여타 구곡에 비해 짧은 것으로 나타났다. 전남지역에서 유일하게 확증된 동악산 3개 구곡의 바위글씨는 총 165건으로 국내에서 가장 많은 바위글씨의 집결지로 판단된다. 특히 112개소로 집계된 청류구곡 바위글씨의 내용 분석결과, '수신(修身)'의 의미가 49점(43.8%)으로 가장 많았으며 다음으로 '인명' 21건(18.8%), '경물' 16건(14.2%), 장구처 등 장구지소' 12건(10.6%) 등이었고 '시구(詩句)'가 차지하는 비율은 6건(3.6%)으로 나타났다. 육로상의 제1곡 쇄연문과 수로상의 제9곡 제시인간별유천(除是人間別有天)은 박세화(朴世和)가 충북 제천에 설정한 용하구곡(用夏九曲)의 제1곡 홍단연쇄(虹斷烟鎖) 및 제9곡 제시인간별유천과 일치하는 것으로 동일한 시원(始原)을 갖는 구곡명으로 유추된다. 또한 육로상 제6곡 대은병(大隱屛)은 주자 무이구곡의 제7곡과 일치하는 것으로 구곡원림의 거점으로 인식되며, 7곡과 8곡 사이의 '암서재(巖棲齋)'와 '포경재(抱經齋)' 바위글씨와 석축 흔적 등은 무이구곡 은병봉 아래 무이정사와 비견되는 것으로, 기호사림의 청류동 활동거점으로 파악된다. 선사어제(鮮史御帝), 보가효우(保家孝友, 고종), 사무사(思無邪, 명나라 의종), 백세청풍(百世淸風, 주자), 청류수석 동악풍경(흥선 대원군) 등 명인들의 명구들이 망라된 동악산 구곡은 높은 유가미학적 가치를 표출함은 물론 의미론적 상징문화경관의 보고라 할 수 있다. 아울러 청류구곡은 수심양성을 위한 유가적 가치체계와 불교 및 도교적 관념 등이 공존하는 유불선(儒彿仙) 3교 문화경관의 결집체로 특성이 부각된다. 청류구곡은 최익현(崔益鉉), 전우(田愚), 기우만(奇宇萬), 송병선(宋秉璿), 황현(黃玹) 등으로 대변되는 조선 후기 사림계층이 성리학의 도통의식을 계승하고 '위정척사'와 '존왕양이(尊王攘夷)', '항일의지 고취' 등의 수단으로 설정되고 활용하는 과정에서 배태(胚胎)된 항일 역사문화 항쟁의 거점으로서 장소성과 의미론적 특성에 충일하다.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.