• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.129-134
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• 2015

Since automatic social engineering based spam attacks induce for users to click or receive the short message service (SMS), e-mail, site address and make a relationship with an unknown friend, it is very easy for them to active in online social networks. The previous spam detection schemes only apply manual filtering of the system managers or labeling classifications regardless of the features of social networks. In this paper, we propose the spam detection metric after reflecting on a couple of features of social networks followed by analysis of real social network data set, Twitter spam. In addition, we provide the online social networks based unsupervised scheme for automated social engineering spam with self organizing map (SOM). Through the performance evaluation, we show the detection accuracy up to 90% and the possibility of real time training for the spam detection without the manager.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.233-238
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• 2020

Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.160-168
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• 2007

For developing a text classifier using supervised learning, a manually labeled corpus of large size is required. However, it takes a lot of time and human effort. Recently a research paradigm was proposed to use a raw corpus and a small amount of seed information instead of manually labeled corpus. In this paper we introduce an unsupervised learning method that makes it possible to achieve better performance than other related works. The characteristics of our approach is that average mutual information is used to learn representative words and their weights and then update of the weights is done using a technique inspired by the works in information retrieval. By iterating this teaming process it was shown that a high performance system can be developed.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.25-29
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• 2005

Microarry 기술의 발전은 유전자의 기능과 상호 관련성 그리고 특성을 파악 가능하게 하였으며, 이를 위한 다양한 분석 기법들이 소개되고 있다. 본 연구에서 소개하는 fuzzy clustering 기법은 genome 영역의 expression 분석에 가장 널리 사용되는 기법중 비지도학습(unsupervized) 분석 기법이다. Fuzzy clustering 기법을 효모(yeast) expression 데이터를 이용하여 분류하여 hard k-means와 비교 하였다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.33-38
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• 2018

In this study, we try to detect new attacks for vehicle by learning only one class. We use Car-Hacking dataset, an intrusion detection dataset, which is used to evaluate classification performance. The dataset are created by logging CAN (Controller Area Network) traffic through OBD-II port from a real vehicle. The dataset have four attack types. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve high efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data, which are new attacks. In this study, we use one class classifier to detect new attacks that are difficult to detect using signature-based rules on network intrusion detection system. The proposed method suggests a combination of parameters that detect all new attacks and show efficient classification performance for normal dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.483-486
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• 2022

최근 이커머스 시장의 지속적인 성장으로 빠른 배송과 대용량 물류 처리를 위한 효율적 배송 시스템 마련의 필요성이 증가하고 있다. 본 연구에서는 도심 물류 거점에서의 현재 배송 물량 할당의 불균등 문제를 실무적 관점에서 정의하고, 비지도 학습 기반 클러스터링 기법을 통해 불균등 배송 할당 문제를 개선해 보고자 했다. 분석 결과 K-means++ 알고리즘 기반 클러스터링에서 최적화된 물량 할당에 대한 개선 가능성을 검증할 수 있었다. 향후 지형 정보, 교통량 등의 상세 변수를 추가하여 머신러닝 기반의 물류 배송 최적화를 위한 연구 영역을 확장할 수 있을 것으로 기대된다.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.31-38
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• 2011

A RAM-based Neural Net is a weightless neural network based on binary neural network. 3-D neural network using this paper is binary neural network with multiful information bits and store counts of training. Recognition method by MRD technique is based on the supervised learning. Therefore neural network by itself can not distinguish between the categories and well-separated categories of training data can achieve only through the performance. In this paper, unsupervised learning algorithm is proposed which is trained existing 3-D neural network without distinction of data, to distinguish between categories depending on the only input training patterns. The training data for proposed unsupervised learning provided by the NIST handwritten digits of MNIST which is consist of 0 to 9 multi-pattern, a randomly materials are used as training patterns. Through experiments, neural network is to determine the number of discriminator which each have an idea of the handwritten digits that can be interpreted.