My list of surveys on anomaly (intrusion) detection:
- Fernandes, G., Rodrigues, J. J., Carvalho, L. F., Al-Muhtadi, J. F., & Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70(3), 447-489. https://link.springer.com/article/10.1007/s11235-018-0475-8
- Their survey analyse the problem on five dimensions: network traffic anomalies, network data types, intrusion detection systems categories, detection methods and systems, and open issues. It highlights that classification methods have the highest detection rates amongst all papers they reviewed.
- Moustafa, N., Hu, J., & Slay, J. (2019). A holistic review of network anomaly detection systems: A comprehensive survey. Journal of Network and Computer Applications, 128, 33-55. https://www.sciencedirect.com/science/article/pii/S1084804518303886
- Cook, A., Mısırlı, G., & Fan, Z. (2019). Anomaly detection for IoT time-series data: A survey. IEEE Internet of Things Journal. https://ieeexplore.ieee.org/iel7/6488907/6702522/08926446.pdf
- Di Mattia, F., Galeone, P., De Simoni, M., & Ghelfi, E. (2019). A survey on gans for anomaly detection. arXiv preprint arXiv:1906.11632. https://arxiv.org/pdf/1906.11632
- Habeeb, R. A. A., Nasaruddin, F., Gani, A., Hashem, I. A. T., Ahmed, E., & Imran, M. (2019). Real-time big data processing for anomaly detection: A survey. International Journal of Information Management, 45, 289-307. https://www.sciencedirect.com/science/article/pii/S0268401218301658
- Basora, L., Olive, X., & Dubot, T. (2019). Recent advances in anomaly detection methods applied to aviation. Aerospace, 6(11), 117. https://www.mdpi.com/2226-4310/6/11/117/pdf
- Adewumi, A. O., & Akinyelu, A. A. (2017). A survey of machine-learning and nature-inspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8(2), 937-953. https://link.springer.com/article/10.1007/s13198-016-0551-y
- Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176. https://ieeexplore.ieee.org/iel7/9739/5451756/07307098.pdf
- Kalinichenko, L., Shanin, I., & Taraban, I. (2014, October). Methods for anomaly detection: A survey. In CEUR Workshop Proceedings (Vol. 1297, p. 2025). http://rcdl.ru/doc/2014/paper/RCDL2014_042-47.pdf
- Wang, J., Rossell, D., Cassandras, C. G., & Paschalidis, I. C. (2013, December). Network anomaly detection: A survey and comparative analysis of stochastic and deterministic methods. In 52nd IEEE Conference on Decision and Control (pp. 182-187). IEEE. https://ieeexplore.ieee.org/iel7/6749719/6759837/06759879.pdf
- Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58. https://dl.acm.org/doi/pdf/10.1145/1541880.1541882
- Sabahi, F., & Movaghar, A. (2008, October). Intrusion detection: A survey. In 2008 Third International Conference on Systems and Networks Communications (pp. 23-26). IEEE. https://www.academia.edu/download/44875590/2005-67.pdf
- Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. https://arxiv.org/pdf/1901.03407
- The survey presents a structured review of research methods in deep anomaly detection and also discusses the adoption of these methods across various application domains and assess their effectiveness.
- Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., & Kim, K. J. (2019). A survey of deep learning-based network anomaly detection. Cluster Computing, 1-13. https://link.springer.com/article/10.1007/s10586-017-1117-8
- This survey views the anomaly detection methods into four categories: statistical anomaly detection, classifier based anomaly detection, anomaly detection using machine learning and finite state machine anomaly detection.
- Agrawal, S., & Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, 708-713. https://www.sciencedirect.com/science/article/pii/S1877050915023479/pdf
- This short work reviews various data mining techniques for anomaly detection
- Zhang, W., Yang, Q., & Geng, Y. (2009, January). A survey of anomaly detection methods in networks. In 2009 International Symposium on Computer Network and Multimedia Technology (pp. 1-3). IEEE. https://ieeexplore.ieee.org/iel5/5374431/5374489/05374676.pdf
- classifies the intrusion into clustering, classification, and hybrid
- Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. expert systems with applications, 36(10), 11994-12000. https://www.sciencedirect.com/science/article/pii/S0957417409004801
- This survey list related studies in the period between 2000 and 2007, viewing them as single, hybrid, and ensemble classifiers using machine learning techniques.