Acknowledgement
본 연구는 교육부와 한국연구재단의 지원을 받아 수행된 사회맞춤형 산학협력 선도대학 (LINC+) 육성사업의 성과물임.
References
- Ahmed, M., Mahmood, A. N., and Hu, J., "A survey of network anomaly detection techniques", Journal of Network and Computer Applications, Vol. 60, pp. 19-31, 2016. https://doi.org/10.1016/j.jnca.2015.11.016
- Alla, S. and Adari, S. K., "Beginning anomaly detection using python-based deep learning," Apress, 2019.
- Cadez, I., Heckerman, D., Meek, C., Smyth, P., and White, S., "Visualization of navigation patterns on a web site using model-based clustering" In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 280-284, 2000.
- Casas, P., Soro, F., Vanerio, J., Settanni, G., and D'Alconzo, A., "Network security and anomaly detection with Big-DAMA, a big data analytics framework," IEEE 6th International Conference on Cloud Networking (CloudNet), pp. 1-7, 2017.
- Cha, B., Park, K., and Seo, J., "Network based anomaly intrusion detection using bayesian network techniques," Journal of Internet Computing and Services, Vol. 6, No. 1, pp. 27-38, 2005.
- Criste, L., "Insider threat market to top $1 billion in fiscal 2020: This is," Available from: https://about.bgov.com/news/insider-threat-market-to-top-1-billion-in-fiscal-2020-this-is/.
- Forrest, S., Hofmeyr, S., Somayaji, A., and Longstaff, T. A., "A sense of self for unix processes," Proceedings 1996 IEEE symposium on security and privacy, pp. 120-128, 1996.
- Habeeb, R. A. A., Nasaruddin, F., Gani, A., Hashem, I. A. T., Ahmed, E., and Imran, M., "Real-time big data processing for anomaly detection: A survey," International Journal of Information Management, Vol. 45, pp. 289-307, 2019. https://doi.org/10.1016/j.ijinfomgt.2018.08.006
- Hofmeyr, S., Forrest, S., and Somayaji, A., "Intrusion detection using sequences of system calls," Journal of computer security, Vol. 6, No. 3, pp. 151-180, 1998. https://doi.org/10.3233/JCS-980109
- Hollmen J. and Tresp, V., "Call-based fraud detection in mobile communication networks using a hierarchical regimeswitching model," In Advances in Neural Information Processing Systems, pp. 889-895, 1999.
- Kang, G.-H., Sohn, J.-M., and Sim, G.-W., "Comparative analysis of anomaly detection models using AE and suggestion of criteria for determining outliers," Journal of Korea Society of Computer Information, Vol. 26, No. 8, pp. 23-30, 2021. https://doi.org/10.9708/JKSCI.2021.26.08.023
- Kim, H., Kim, J., Park, M, Cho, S., and Kang, P., "Insider threat detection based on user behavior model and novelty detection algorithms," Journal of the Korean Institute of Industrial Engineers, Vol. 43, No. 4, pp. 276-287, 2017. https://doi.org/10.7232/JKIIE.2017.43.4.276
- Lee, J. and Lee, K. Y., "An anomalous sequence detection method based on an extended LSTM autoencoder," The Journal of Society for e-Business Studies, Vol. 26, No. 1, pp.127-140, 2021. https://doi.org/10.7838/JSEBS.2021.26.1.127
- Liang, N. Biros, D. P., and Luse, A., "An empirical validation of malicious insider characteristics," Journal of Management Information Systems, Vol. 33, No. 2, pp. 361-392, 2016. https://doi.org/10.1080/07421222.2016.1205925
- Lopez, E. and Sartip, K., "Detecting the insider's threat with long short term memory (LSTM) neural networks," arXiv, 2007. 11956.
- Roh, K.-W., Kim, J.-S., and Cho, W.-S., "A Study on the design of supervised and unsupervised learning models for fault and anomaly detection in manufacturing facilities," The Journal of Bigdata, Vol. 6, No. 1, pp. 23-35, 2021.
- Smyth, P., "Clustering sequences with hidden markov models," Advances in Neural Information Processing Systems, pp. 648-654, 1997.
- Theoharidou, M., Kokolakis, S., Karyda, M., and Kiountouzis, E., "The insider threat to information systems and the effectiveness of ISO17799," Computers & Security, Vol. 24, No. 6, pp. 472-484, 2005. https://doi.org/10.1016/j.cose.2005.05.002
- Vanerio, J. and Casas, P., "Ensemble-learning approaches for network security and anomaly detection," Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, pp. 1-6, 2017.
- Warrender, C., Forrest, S., and Pearlmutter, B., "Detecting intrusions using system calls: Alternative data models," Proceedings of the 1999 IEEE symposium on security and privacy, pp. 133-145, 1999.
- Xu, K., Tian, K., Yao, D., and Ryder, B.., "A sharper sense of self: Probabilistic reasoning of program behaviors for anomaly detection with context sensitivity," 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 467-478, 2016.
- Xu, K., Yao, D. D., Ryder, B. G., and Tian, K., "Probabilistic program modeling for high-precision anomaly classification" Computer Security Foundations Symposium (CSF), IEEE 28th. pp.497-511, 2015.
- Yao, D., Shu, X., Cheng, L., and Stolfo, S. J., "Anomaly detection as a service: Challenges, advances, and opportunities," Morgan & Claypool, 2017.
- Yeung, D.-Y. and Ding, Y., "Host-based intrusion detection using dynamic and static behavioral models," Pattern Recognition, Vol. 36, No. 1, pp. 229-243, 2003. https://doi.org/10.1016/S0031-3203(02)00026-2