| A survey of deep learning-based network anomaly detection D Kwon, H Kim, J Kim, SC Suh, I Kim, KJ Kim Cluster Computing 22, 949-961, 2019 | 826 | 2019 |
| An empirical study on network anomaly detection using convolutional neural networks D Kwon, K Natarajan, SC Suh, H Kim, J Kim 2018 IEEE 38th International Conference on Distributed Computing Systems …, 2018 | 164 | 2018 |
| An empirical evaluation of deep learning for network anomaly detection RK Malaiya, D Kwon, SC Suh, H Kim, I Kim, J Kim IEEE Access 7, 140806-140817, 2019 | 123 | 2019 |
| An encoding technique for CNN-based network anomaly detection T Kim, SC Suh, H Kim, J Kim, J Kim 2018 IEEE International Conference on Big Data (Big Data), 2960-2965, 2018 | 72 | 2018 |
| Parallel in situ indexing for data-intensive computing J Kim, H Abbasi, L Chacon, C Docan, S Klasky, Q Liu, N Podhorszki, ... 2011 IEEE Symposium on Large Data Analysis and Visualization, 65-72, 2011 | 71 | 2011 |
| Enhancing IoT anomaly detection performance for federated learning B Weinger, J Kim, A Sim, M Nakashima, N Moustafa, KJ Wu Digital Communications and Networks 8 (3), 314-323, 2022 | 57 | 2022 |
| Energy proportionality for disk storage using replication J Kim, D Rotem Proceedings of the 14th International Conference on Extending Database …, 2011 | 46 | 2011 |
| Federated wireless network intrusion detection B Cetin, A Lazar, J Kim, A Sim, K Wu 2019 IEEE International Conference on Big Data (Big Data), 6004-6006, 2019 | 45 | 2019 |
| Unsupervised labeling for supervised anomaly detection in enterprise and cloud networks S Baek, D Kwon, J Kim, SC Suh, H Kim, I Kim 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing …, 2017 | 45 | 2017 |
| 5g-nidd: A comprehensive network intrusion detection dataset generated over 5g wireless network S Samarakoon, Y Siriwardhana, P Porambage, M Liyanage, SY Chang, ... arXiv preprint arXiv:2212.01298, 2022 | 42 | 2022 |
| Energy-aware scheduling in disk storage systems J Chou, J Kim, D Rotem 2011 31st International Conference on Distributed Computing Systems, 423-433, 2011 | 36 | 2011 |
| Anomaly detection based on traffic monitoring for secure blockchain networking J Kim, M Nakashima, W Fan, S Wuthier, X Zhou, I Kim, SY Chang 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-9, 2021 | 35 | 2021 |
| Applying data mining techniques to analyze alert data M Shin, HS Moon, KH Ryu, KY Kim, JO Kim Web Technologies and Applications: 5th Asia-Pacific Web Conference, APWeb …, 2003 | 29 | 2003 |
| Multivariate network traffic analysis using clustered patterns J Kim, A Sim, B Tierney, S Suh, I Kim Computing 101, 339-361, 2019 | 27 | 2019 |
| Using data accessibility for resource selection in large-scale distributed systems J Kim, A Chandra, JB Weissman IEEE Transactions on Parallel and Distributed Systems 20 (6), 788-801, 2009 | 23 | 2009 |
| Energy proportionality and performance in data parallel computing clusters J Kim, J Chou, D Rotem Scientific and Statistical Database Management: 23rd International …, 2011 | 22 | 2011 |
| Automated feature selection for anomaly detection in network traffic data M Nakashima, A Sim, Y Kim, J Kim, J Kim ACM Transactions on Management Information Systems (TMIS) 12 (3), 1-28, 2021 | 19 | 2021 |
| Botnet detection using recurrent variational autoencoder J Kim, A Sim, J Kim, K Wu GLOBECOM 2020-2020 IEEE Global Communications Conference, 1-6, 2020 | 19 | 2020 |
| A machine learning approach to anomaly detection based on traffic monitoring for secure blockchain networking J Kim, M Nakashima, W Fan, S Wuthier, X Zhou, I Kim, SY Chang IEEE Transactions on Network and Service Management 19 (3), 3619-3632, 2022 | 16 | 2022 |
| Exploiting replication for energy-aware scheduling in disk storage systems JCY Chou, TH Lai, J Kim, D Rotem IEEE Transactions on Parallel and Distributed Systems 26 (10), 2734-2749, 2014 | 16 | 2014 |
Alex SimLawrence Berkeley National Laboratory在 lbl.gov 的电子邮件经过验证
