International Journal Articles & Conference Papers
[11] Miru Kim, Heewon Park, Minhae Kwon, Anonymous submission – federated learning, Top AI Conference, under review.
[10] Heewon Park, Mugon Joe, Miru Kim, Minhae Kwon, Anonymous submission – federated learning, Top AI Conference, under review.
[9] Miru Kim*, Mugon Joe*, Minhae Kwon, "Network Attack Classification under Label Shift: Contrastive Pre-training and LoRA-based Adaptation," IEEE Transactions on Network Science and Engineering, Accepted. (*equal contribution) (Rating: Q1, Rank: 3/136-Top 1.8%, IF: 7.9)
[8] Mugon Joe*, Miru Kim*, Minhae Kwon, "Contrastive Learning Based Network Attack Classifier for Imbalanced Data," Journal of Communications and Networks (JCN), Accepted. (*equal contribution)
[7] Miru Kim*, Heewon Park*, Minhae Kwon, “Personalized Split Federated Learning with Early-exit: Pre-training and Online Learning Against Label Shifts,” IEEE IoT Journal, Early Access. (*equal contribution) [link] (Rating: Q1, Rank: 9/250-Top 3.4%, IF: 8.2)
[6] Miru Kim, Mugon Joe, Minhae Kwon, "OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware System," ACM International Conference on Information and Knowledge Management (ACM CIKM), Aug. 2025. (BK21+ IF: 3, Acceptance rate: 27%)
[5] Miru Kim, Mugon Joe, Minhae Kwon, "Improving Network Attack Classification on Imbalanced Real-world Intrusion Incident Datasets," ACM International Conference on Mobile Systems, Applications, and Services (ACM MobiSys), Jun. 2025. (BK21+ IF: 3)
[4] Heewon Park, Mugon Joe, Miru Kim, Minhae Kwon, "ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate," ACM International Conference on Information and Knowledge Management (ACM CIKM), Aug. 2025. (BK21+ IF: 3, Acceptance rate: 30.6%)
[3] Heewon Park*, Miru Kim*, Minhae Kwon, "Personalized Federated Sensing for Heterogeneous Environment," IEEE Sensors Letters (IEEE SL), vol. 9, no. 4, 2024. (*equal contribution) [link]
[2] Hyoseon Kye, Miru Kim, Minhae Kwon, "Hierarchical Detection of Network Anomalies: A Self-supervised Learning Approach," IEEE Signal Processing Letters (IEEE SPL), vol. 29, pp. 1908-1912, 2022. [link][Newsis][AItimes]
[1] Hyoseon Kye, Miru Kim, Minhae Kwon, “Hierarchical Autoencoder for Network Intrusion Detection,” IEEE International Conference on Communications (IEEE ICC), May. 2022. [link][Media]
International Conference Workshop Papers
[3] Miru Kim, Minhae Kwon, "Network Traffic Foundation Model with Adaptation via In-Context Learning and Mixture-of-Experts," Conference on Neural Information Processing Systems (NeurIPS) AI4NextG Workshop, Dec 2025.
[2] Miru Kim, Minhae Kwon, “Augmenting the Knowledge to Large Model from Federated Small Models,” International Conference on Machine Learning (ICML) workshop, July 2023.
[1] Heewon Park, Miru Kim, Minhae Kwon, “Localizing Partial Model for Personalized Federated Learning,” International Conference on Machine Learning (ICML) workshop, July 2023.
Domestic Journal Articles
[3] Mugon Joe, Miru Kim, Minahe Kwon, "Fine-tuning Anomaly Classifier for Unbalanced Network Data," Journal of Korean Institute of Communications and Information Sciences (JKICS), vol. 49, no. 7, Jul. 2023. (Best Paper) [pdf]
[2] Heeson Park, Miru Kim, Minhae Kwon, "Robust Partial Share Federated Learning Algorithm against Model Poisoning Attack," Journal of Korean Institute of Communications and Information Sciences (JKICS), vol. 48, no. 11, Nov. 2023. [pdf]
[1] Miru Kim, Hyoseon Kye, Minhae Kwon, "Network Anomaly Detection System Using Hidden Layer Information of Autoencoder," Journal of Korean Institute of Communications and Information Sciences (JKICS), vol. 47, no. 9, Sep. 2022. [pdf]
Domestic Conference Papers
[17] Kyungjin Im, Heewon Park, Miru Kim, Mugon Joe, Minhae Kwon, “FIM-based Personalized Federated Post-training Solution Robust to Data Distribution Shift,” Korea Computer Congress, July 2025. (Best Paper Award) [Video]
[16] Kyungjin Im, Heewon Park, Miru Kim, Minhae Kwon, “Federated Post-training for Personalized Client Models,” the Joint Conference on Communications and Information, Jul. 2025. [Video]
[15] Miru Kim, Minhae Kwon, "xLSTM-based Autoencoder for Anomaly Detection of Time Series Network Traffic Data, " Korean Institute of Communications and Information Sciences Winter Conference, Feb. 2025. [pdf] [video]
[14] Miru Kim, Heewon Park, Minhae Kwon, "Federated Learning for Multiple SDVs and Cloud Collaborative Systems," Korean Society of Automotive Engineers Autumn Conference, Nov. 2024. [pdf] [video]
[13] Heewon Park, Miru Kim, Minhae Kwon, "Robust Federated Learning for SDV Against Adversarial Poisoning Attack," Korean Society of Automotive Engineers Autumn Conference, Nov. 2024. [pdf]
[12] Miru Kim, Heewon Park, Minhae Kwon "Online Learning for SDV and Cloud AI Models against Data Distribution Shifts," Korean Society of Automotive Engineers Autumn Conference, Nov. 2024. [pdf]
[11] Mugon Joe, Miru Kim, Minhae Kwon, "Contrastive Learning Based Adaptation Algorithms for CAN Intrusion Detection on Shifting Attack Types," Korean Society of Automotive Engineers Autumn Conference, Nov. 2024. [pdf]
[10] Mugon Joe, Miru Kim, Minhae Kwon, "Hierarchical Autoencoder Based Network Intrusion Detection Systems for CAN Communications," Korean Society of Automotive Engineers Autumn Conference, Nov. 2024. [pdf]
[9] Mugon Joe, Miru Kim, Minhae Kwon, "Fine-tuning Solution for Hierarchical Autoencoder Anomaly Classifiers," Korean Institute of Communications and Information Sciences Fall Conference, Nov. 2023. [pdf]
[8] Heewon Park, Miru Kim, Minhae Kwon, "Robust Partial Share Federated Learning Against Model Poisoning Attack," Korean Institute of Communications and Information Sciences Summer Conference, Jun. 2023. (Best Paper Award) [pdf]
[7] Heewon Park, Miru Kim, Minhae Kwon, "Performance Comparison of Partial Share Algorithms for Personalized Federated Learning," Joint Conference on Communications and Information, Apr. 2023. [pdf]
[6] Miru Kim, Minhae Kwon, "Performance Analysis of Partial-share Solution for Personalized Federated Learning," Korean Institute of Communications and Information Sciences Winter Conference, Feb. 2023. [pdf] [video]
[5] Miru Kim, Minhae Kwon, "Low-complex Anomaly Detection Method Using Important Features," Korea Artificial Intelligence Conference, Sep. 2022. [pdf] [video]
[4] Miru Kim, Hyoseon Kye, Sujin Ahn, Minhae Kwon, “Performance Comparison of Neural Network Models for Network Intrusion System,” Joint Conference on Communications and Information, Apr. 2022. [pdf] [video]
[3] Sujin Ahn, Hyoseon Kye, Miru Kim, Minhae Kwon, “Study on High-impact Features in Multiple Autoencoder Neural Network Models,” Joint Conference on Communications and Information, Apr. 2022. [pdf]
[2] Miru Kim, Hyoseon Kye, Minhae Kwon, “A Study on Detection Role of Hierarchical Stage in Autoencoder Based Network Intrusion Detection Systems,” Korean Institute of Communications and Information Sciences Winter Conference, Feb. 2022. (Best Paper Award) [pdf] [video]
[1] Miru Kim, Hyoseon Kye, Minhae Kwon, “RaPP-based Network Anomaly Detection Systems,” Korea Artificial Intelligence Conference, Sep. 2021. [pdf] [video]