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Effective Embedding Techniques for Misbehavior Classification in Vehicular Ad-Hoc Networks
http://doi.org/10.5626/JOK.2024.51.11.970
Vehicular Ad-Hoc Networks (VANET) is a network technology enabling data transmission between vehicles that includes V2X communication, which facilitates the exchange of both external and internal vehicle information based on communication between vehicles, infrastructure, and pedestrians. However, broadcasting data containing faults or attack information within the network can lead to critical issues, making Misbehavior Detection (MBD) systems an essential technology in VANET. While recent studies have increasingly employed machine learning for MBD, the patterns of misbehavior types in VANET often resemble normal behavior, posing challenges for comprehensive and accurate classification. Existing research has suggested a hierarchical classification system to categorize misbehaviors based on different types of attacks and faults. This study proposed an embedding representation method for constructing a hierarchical classification system to improve the accuracy of misbehavior classification models. By extracting embedding vectors for multivariate time-series data through a pre-trained LSTM model, this study compressed core data related to misbehavior types and employed hierarchical clustering to group various attack types into broader categories.
ECG Arrhythmia Classification Model with VAE-based Data Augmentation and CNN
http://doi.org/10.5626/JOK.2023.50.11.947
Due to its convenient accessibility, and crucial importance in arrhythmia diagnosis, ECG data is often considered in predicting heart disease. The MIT-BIH Arrhythmia dataset, which is widely utilized in research focused on arrhythmia analysis, is one of the contributing factors to heart disease. However, the dataset exhibits imbalanced arrhythmia classes due to variations in incidence rate. These imbalanced arrhythmia classes affect the performance of arrhythmia classification. To solve the imbalanced problem, this paper presents four distinct classification methods that utilize augmented data. These different augmentation techniques were compared and assessed alongside the VAE method in terms of classification performance. Furthermore, the CNN and the CNN-LSTM models were compared and analyzed in the context of the classification model. In conclusion, by applying VAE augmentation to train the balanced data and classifying the arrhythmia using the CNN, we achieved an accuracy of 98.9%. These results confirm the superior effectiveness of the proposed model compared to other existing arrhythmia classification models, particularly in terms of the sensitivity.
Comparison of BERT-based Model Performance in CBCA Criteria Classification
Junho Shin, Jungsoo Shin, Eunkyung Jo, Yeohoon Yoon, Jaehee Jung
http://doi.org/10.5626/JOK.2022.49.9.727
In the case of child sex crimes, the victim"s statement plays a critical role in determining the existence or innocence of the case, so the Supreme Prosecutors" Office classifies the statement into a total of 19 criteria according to Criteria-Based Content Analysis (CBCA), a victim"s statement analysis technique. However, this may differ in criteria classification according to the subjective opinion of the statement analyst. Thus, in this paper, two major classification methods were applied and analyzed to present an criteria classification model using BERT and RoBERTa. The two methods comprise of a method of classifying the entire criterion at the same time, as well as method of dividing it into four groups, and then classifying the criteria within the group secondarily. The experiment classified statements into 16 criteria of CBCA and performed comparative analysis using several pre-trained models. As a result of the classification, the former classification method performed better than the latter classification method in 13 of the total 16 criteria, and the latter method was effective in three criteria with a relatively insufficient number of training data. Additionally, the RoBERTa-based model performed better than the BERT-based model in 15 of the 16 criteria, and the BERT model, which was pre-trained using only Korean conversational colloquial language, classified the remaining one criterion uniquely. This paper shows that the proposed model, which was pre-trained using interactive colloquial data is effective in classifying children"s statement sentences.
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