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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.
Comparing Initiating and Responding Joint Attention as a Social Learning Mechanism : A Study Using Human-Avatar Head/Hand Interaction
Mingyu Kim, So-Yeon Kim, Kwanguk Kim
Joint Attention (JA) has been known to play a key role in human social learning. However, relative impact of different interaction types has yet to be rigorously examined because of limitation of existing methodologies to simulate human-to-human interaction. In the present study, we designed a new JA paradigm with emulating human-avatar interaction and virtual reality technologies, and tested the paradigm in two experiments with healthy adults. Our results indicated that initiating JA (IJA) condition was more effective than responding JA (RJA) condition for social learning in both head and hand interactions. Moreover, the hand interaction involved better information processing than the head interaction. The implication of the results, the validity of the new paradigm, and limitations of this study were discussed.
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