APPLICATION OF ARTIFICIAL INTELLIGENCE IN DETECTION OF DDoS ATTACKS
Services distributed over the Internet are ranging from entertaining and informative to those whose availability must not be interrupted because it affects the quality of life, but also safety and health. Due to its importance, the global computer network is a desirable target, attacks are continually taking place, and the damage is more than considerable. Among the many types of attacks, one of the most effective, given the relationship between the damage done and the challenge to be prevented, detect and control, are DDoS attacks. This paper discusses the phases, components, categories, and types of DDoS attacks and emphasizes detection approaches. The standout approach and one that can answer the complexity of detecting DDoS attacks is the classification with artificial intelligence techniques. This work shows why artificial intelligence represents the starting point for further research in information security.
2. Doriguzzi-Corin, R., Millar, S., Scott-Hayward, S., Martínez-del-Rincón, J. & Siracusa, D. (2020). Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection. IEEE Transactions on Network and Service Management, 17 (2), 876-889.
3. Han, D., Bi, K., Liu, H. & Jia J. (2017). A DDoS Attack Detection System Based on Spark Framework. Computer Science and Information Systems, 14 (3), 769-788.
4. Kadhem, H., Amagasa, T. & Kitagawa, H. (2009). A Novel Framework for Database Security Based on Mixed Cryptography. Fourth International Conference on Internet and Web Applications and Services, Venice/Mestre, Italy, 163-170.
5. Kong, B., Yang, K., Sun, D., Li, M. & Shi, Z. (2017). Distinguishing Flooding Distributed Denial of Service from Flash Crowds Using Four Data Mining Approaches. Computer Science and Information Systems, 14 (3), 839-856.
6. Kumar, G., Kumar, K. & Sachdeva, M. (2010). The use of artificial intelligence-based techniques for intrusion detection: a review. Artificial Intelligence Review, 34 (4), 369-387.
7. Liao, H. J., Lin, C. H. R., Lin, Y. C. & Tung, K. Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36, 16-24.
8. Liang, X. & Znati, T. (2019). On the performance of intelligent techniques for intensive and stealthy DDos detection. Computer Networks, 164, 106906.
9. Modi, C., Patel, D., Patel, H., Borisaniya, B., Patel, A. & Rajarajan, M. (2013). A survey of intrusion detection techniques in Cloud. Journal of Network and Computer Applications, 36 (1), 42-57.
10. Napanda, K., Shah, H. & Kurup, L. (2015). Artificial Intelligence Techniques for Network Intrusion Detection. International Journal of Engineering Research & Technology, 4 (11), 357-361.
11. Peng, T., Leckie, C. & Ramamohanarao, K. (2007). Survey of network-based defense mechanisms countering the DoS and DDoS problems. ACM Computing Surveys, 39 (1), 1-42.
12. Petkovic, M., Basicevic, I., Kukolj D. & Popovic, M. (2018). Evaluation of Takagi-Sugeno-Kang fuzzy method in entropy-based detection of DDoS attacks. Computer Science and Information Systems, 15 (1), 139-162.
13. Polat, H., Polat, O. & Çetin, A. (2020). Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models. Sustainability, 12, 1035.
14. Saied, A., Overill, R. E. & Radzik, T. (2016). Detection of known and unknown DDoS attacks using Artificial Neural Networks. Neurocomputing, 172, 385–393.
15. Srivastava, A., Gupta, B. B., Tyagi, A., Sharma, A. & Mishra, A. (2011). A Recent Survey on DDoS Attacks and Defense Mechanisms. International Conference on Parallel Distributed Computing Technologies and Applications, Advances in Parallel Distributed Computing, 570-580.
16. Stalings, W. (2011). Cryptography and network security principles and practices. Prentice Hall, New York City, USA.
17. 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, 11994-12000.
18. Wang, M., Luo, Y. & Zhong, H. (2019). DDoS detection and defense mechanism based on cognitive-inspired computing in SDN. Future Generation Computer Systems 97, 275–283.