Computer and Knowledge Engineering

Article 1, Volume 7, Issue 1 - Serial Number 13, January 0, Pages 1-8   

Document Type: Original Article

DOI: 10.22067/cke.2024.82986.1084

References

[1]   M. Aamir and S. M. Ali Zaidi, “Clustering based semi-supervised machine learning for DDoS attack classification,” Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 4, May 2021, doi: 10.1016/j.jksuci.2019.02.003.

[2]   S. Zavrak and M. Iskefiyeli, “Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3001350.

[3]   R. Bhatia, R. Sharma, and A. Guleria, “Anomaly Detection Systems Using IP Flows: A Review,” 2021. doi: 10.1007/978-981-16-0235-1_80.

[4]   M. M. Hassan, A. Gumaei, A. Alsanad, M. Alrubaian, and G. Fortino, “A hybrid deep learning model for efficient intrusion detection in big data environment,” Information Sciences, vol. 513, Mar. 2020, doi: 10.1016/j.ins.2019.10.069.

[5]   S.-T. Chiu and F.-Y. Leu, “Detecting DoS and DDoS Attacks by Using CuSum Algorithm in 5G Networks,” 2021. doi: 10.1007/978-3-030-57811-4_1.

[6]   M. Nooribakhsh and M. Mollamotalebi, “A review on statistical approaches for anomaly detection in DDoS attacks,” Information Security Journal: A Global Perspective, vol. 29, no. 3, May 2020, doi: 10.1080/19393555.2020.1717019.

[7]   S. Hosseini and M. Azizi, “The hybrid technique for DDoS detection with supervised learning algorithms,” Computer Networks, vol. 158, Jul. 2019, doi: 10.1016/j.comnet.2019.04.027.

[8]   M. Du, N. Liu, and X. Hu, “Techniques for interpretable machine learning,” Commun. ACM, vol. 63, no. 1, pp. 68–77, 2020, doi: 10.1145/3359786.

[9]   C. Yin, Y. Zhu, J. Fei, and X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks,” IEEE Access, vol. 5, pp. 21954–21961, Oct. 2017, doi: 10.1109/ACCESS.2017.2762418.

[10] M. M. Hassan, A. Gumaei, A. Alsanad, M. Alrubaian, and G. Fortino, “A hybrid deep learning model for efficient intrusion detection in big data environment,” Information Sciences, vol. 513, pp. 386–396, Mar. 2020, doi: 10.1016/j.ins.2019.10.069.

[11] A. Girma, M. Garuba, Jiang Li, and Chunmei Liu, “Analysis of DDoS Attacks and an Introduction of a Hybrid Statistical Model to Detect DDoS Attacks on Cloud Computing Environment,” Apr. 2015. doi: 10.1109/ITNG.2015.40.

[12] R. B. Blažek, H. Kim, B. Rozovskii, and A. Tartakovsky, “A novel approach to detection of ‘denial-of-service’ attacks via adaptive sequential and batch-sequential change-point detection methods,” 2001.

[13] S. R. Gaddam, V. v Phoha, and K. S. Balagani, “K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods.”

[14] Hoai-Vu Nguyen and Yongsun Choi, “Proactive Detection of DDoS Attacks Utilizing k-NN Classifier in an Anti-DDos Framework,” World Academy of Science, Engineering and Technology , 2010.

[15] A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava, “A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection,” May 2003. doi: 10.1137/1.9781611972733.3.

[16] C.-K. Han and H.-K. Choi, “Effective discovery of attacks using entropy of packet dynamics,” IEEE Network, vol. 23, no. 5, Sep. 2009, doi: 10.1109/MNET.2009.5274916.

[17] C. Di Francescomarino and C. Ghidini, “Predictive Process Monitoring,” in Lecture Notes in Business Information Processing, 2022, vol. 448, pp. 320–346. doi: 10.1007/978-3-031-08848-3_10.

[18] W. Rizzi, C. Di Francescomarino, and F. M. Maggi, “Explainability in predictive process monitoring: When understanding helps improving,” in Lecture Notes in Business Information Processing, 2020, vol. 392 LNBIP, pp. 141–158. doi: 10.1007/978-3-030-58638-6_9.

[19] R. Sindhgatta, C. Ouyang, and C. Moreira, “Exploring interpretability for predictive process analytics,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, vol. 12571 LNCS, pp. 439–447. doi: 10.1007/978-3-030-65310-1_31.

[20] D. Adi and N. Nurdin, “Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task,” IPTEK J. Eng., vol. 7, no. 1, p. 11, 2021, doi: 10.12962/j23378557.v7i1.a8989.

[21] I. Sharafaldin, A. H. Lashkari, S. Hakak, and A. A. Ghorbani, “Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy,” in Proc. 53rd International Carnahan Conference on Security Technology, Chennai, India, 2019.

[22] W. E. Marcilio and D. M. Eler, “From explanations to feature selection: Assessing SHAP values as feature selection mechanism,” in Proceedings - 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2020, 2020, pp. 340–347. doi: 10.1109/SIBGRAPI51738.2020.00053..

[23] Mirkovic, Jelena, Gregory Prier, and Peter Reiher. "Attacking DDoS at the source." 10th IEEE International Conference on Network Protocols, 2002. Proceedings.. IEEE, 2002.

[24] J. Mirkovic, G. Prier, and P. Reiher, “Source-end DDoS defense,” in Second IEEE International Symposium on Network Computing and Applications, 2003. NCA 2003., pp. 171–178. doi: 10.1109/NCA.2003.1201153.

[25] S. I. Ao and International Association of Engineers., International MultiConference of Engineers and Computer Scientists : IMECS 2009 : 18-20 March, 2009, Regal Kowloon Hotel, Kowloon, Hong Kong. Newswood Ltd., 2009.

[26] X. Liang and T. Znati, “On the performance of intelligent techniques for intensive and stealthy DDos detection,” Computer Networks, vol. 164, Dec. 2019, doi: 10.1016/j.comnet.2019.106906.

[27] X. Wu et al., “Top 10 algorithms in data mining,” Knowledge and Information Systems, vol. 14, no. 1, Jan. 2008, doi: 10.1007/s10115-007-0114-2.

[28] D. Hu, P. Hong, and Y. Chen, “FADM: DDoS Flooding Attack Detection and Mitigation System in Software-Defined Networking,” Dec. 2017. doi: 10.1109/GLOCOM.2017.8254023.

[29] Z. Xie, W. Dong, J. Liu, H. Liu, and D. Li, “Tahoe,” in Proceedings of the Sixteenth European Conference on Computer Systems, Apr. 2021, pp. 426–440. doi: 10.1145/3447786.3456251.

[30] B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.

[31] S. K. Murthy, “Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey,” Data Mining and Knowledge Discovery, vol. 2, no. 4, 1998, doi: 10.1023/A:1009744630224.

[32] H. Kousar, M. M. Mulla, P. Shettar, and D. G. Narayan, “Detection of DDoS Attacks in Software Defined Network using Decision Tree,” in 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Jun. 2021, pp. 783–788. doi: 10.1109/CSNT51715.2021.9509634.

[33] Gao, W. and Morris, T.H., 2014. On cyber attacks and signature based intrusion detection for modbus based industrial control systems. Journal of Digital Forensics, Security and Law, 9(1), p.3.