Predicting and Preventing DDoS Attacks with Deep Learning

By: Nicko Cajes; Northern Bukidnon State College, Philippines

Abstract

The emergence of Distributed Denial of Service (DDoS) attacks has been one of the most significant threats for businesses and organizations in this modern era. Together with the advancement of technology, Deep Learning (DL) has also emerged as an effective countermeasure against this attack due to its unique characteristic. Its novel methods like the Convolutional Neural Network (CNN) and Deep Neural Network (DNN) have demonstrated some excellent results when employed against DDoS attacks, offering great advancement and mitigation opportunities in real-time.

Introduction

A digital attack known as a Distributed Denial of Service (DDoS) takes place when numerous devices target a particular server. In most cases, when performing such things, a server’s network is overloaded, and no additional data is sent to it. This kind of assault can be launched by laptops, PCs, or even individual gadgets like cell phones [1]. This type of attack can give a huge impact especially in the context of businesses and large organizations, as it will make their services unavailable dealing huge damages and possible financial loss [2]. The huge threat that it provides has introduced a lot of innovation in terms of countering the attack, one of those is the Deep Learning (DL) [3]. DL is a recent development in machine learning that incorporates several neural networks, this was made up of input layer, hidden layers, and an output layer [4], it can deal with huge amounts of dataset making it a good option against this attack. This article will discuss how DL can help in predicting and preventing the DDoS attacks, highlighting its effective methods and unique approaches.

Deep Learning for DDoS Detection

Deep Learning has a lot of methods in the bag that can be used against sophisticated cyber-attacks nowadays. Two of the most common methods are the Convolutional Neural Network (CNN) and Deep Neural Network (DNN), which have their own distinct strengths in effectively detecting malicious attacks.

CNN: CNNs are built around neurons which are laid out in layers and may therefore understand hierarchical illustrations, just like any other model of the neural network category [5]. Weights and biases have been employed to link the neurons in different levels. The input layer, which includes data collected via remote sensing, is the first layer. The output layer, which includes an assumed segmentation towards plant categories, is the last layer. There are hidden layers between each other that change each input’s feature set to correspond with the output. In order to take advantage of trends, CNNs incorporate a minimum of a single convolutional layer as a hidden layer [6].

DNN: The DNN represents a neural network made up of neurons that has at least three hidden layers that exists between the input and output layers. In contrast to deep neural networks, typical neural networks include two or fewer hidden layers [7].

Key Phase in Predicting DDoS Attacks with Deep Learning

A Key phase in developing a Deep Learning prediction system happened in the primary phase of development, specifically in the data collection and model training as it plays a crucial role on how the model will perform in the latter phases.

Data Collection: To create a model based on data targeting a certain issue area, deep learning usually requires a lot of data. The explanation lies in the fact that deep learning algorithms frequently exhibit inadequate efficiency whenever the number of data is minimal [8]. However, if the right guidelines are followed in these situations, the typical machine-learning methods’ efficiency can be enhanced.

Model Training: Because deep learning algorithms have many variables, training them usually requires an extended duration, hence, the models training procedure becomes lengthier. For example, ML algorithm training requires just a few seconds to hours, but DL models might take as long as seven days to finish the training phase. When correlated to some ML techniques, DL algorithms operate incredibly quickly around testing [9].

These phases have become crucial in the development phase as the behavior of the model varies a lot on what has been done during this step, this makes it one of the carefully handled areas when attempting to develop a DL model against DDoS attack. In line with this, many researchers have already implemented and developed such approaches just like the study conducted by [10] who utilize DL models and achieved an excellent accuracy of 99% on distinguishing unseen traffic which can help in identifying DDoS attacks.

Preventing DDOS Attack Using Deep Learning

Prevention of DDoS attacks against DDoS utilizing Deep learning models have become successful due to its unique characteristics that differentiate them from conventional detection methods. DL has the ability to detect DDoS attacks in real-time, highlighting its ability to quickly mitigate the attack before it penetrates deeply. Such study was already done by [11] who utilized various machine learning and DL algorithms to employ a real-time DDoS detection system, successfully detecting DDoS attacks such as SYN flood with an excellent accuracy of 98% highlighting its practical application and successful implementation.

Due to the fact that DL is part of AI and a subset of ML, integrating with the current security systems which was mostly driven by AI have not become a problem. Number of researchers have already done that and have achieved a good performance in comparison to the prior approach. [12] have made a detection and prevention system for DoS attack which integrates DL model to the intrusion detection system, their study has achieved an overall accuracy of 99.4% which is relatively high and have reached almost a 100%, this represents the successful compatibility of DL approaches to the existing security systems.

Conclusion

Deep Learning has provided an effective security mechanism through accurately predicting DDoS attacks. By providing high accuracy and efficient performance, DL methods like CNN and DNN have strongly demonstrated their capability of coping up with the sophistication of DDoS attack threats. Their ability to be easily integrated in the current security systems have also highlighted its compatibility and will greatly aid in creating a robust defense mechanism against the ever-evolving cyber-threats.

Reference

1. Dahiya, A., & Gupta, B. B. (2021). How iot is making ddos attacks more dangerous. Insights2Techinfo.[Online.
2. Salih, A., Zeebaree, S. T., Ameen, S., Alkhyyat, A., & Shukur, H. M. (2021, February). A survey on the role of artificial intelligence, machine learning and deep learning for cybersecurity attack detection. In 2021 7th International Engineering Conference “Research & Innovation amid Global Pandemic”(IEC) (pp. 61-66). IEEE.
3. Pappachan, P., Adi, N. S., Firmansyah, G., & Rahaman, M. (2025). Deep Learning-Based Forensics and Anti-Forensics. In Digital Forensics and Cyber Crime Investigation (pp. 211-240). CRC Press.
4. M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali and M. Guizani, “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security”, IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1646-1685, 2020.
5. Vajrobol, V., Saxena, G. J., Pundir, A., Singh, S., B. Gupta, B., Gaurav, A., & Rahaman, M. (2024). Identify spoofing attacks in Internet of Things (IoT) environments using machine learning algorithms. Journal of High Speed Networks, 09266801241295886.
6. Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing, 173, 24-49.
7. Subasi, A. (2020). Machine learning techniques. Practical machine learning for data analysis using Python, 91-202.
8. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
9. Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Gao M, Hou H, Wang C. Machine learning and deep learning methods for cybersecurity. Ieee access. 2018;6:35365–81.
10. Yungaicela-Naula, N. M., Vargas-Rosales, C., & Perez-Diaz, J. A. (2021). SDN-based architecture for transport and application layer DDoS attack detection by using machine and deep learning. IEEE Access, 9, 108495-108512.
11. Musumeci, F.; Fidanci, A.C.; Paolucci, F.; Cugini, F.; Tornatore, M. Machine-Learning-enabled DDoS Attacks Detection in P4 Programmable Networks. J. Netw. Syst. Manag. 2022, 30, 1–27. [CrossRef]
12. Garcia, J. F. C., & Blandon, G. E. T. (2022). A deep learning-based intrusion detection and preventation system for detecting and preventing denial-of-service attacks. IEEE Access, 10, 83043-83060.
13. Dahiya, A., & Gupta, B. B. (2021). A reputation score policy and Bayesian game theory based incentivized mechanism for DDoS attacks mitigation and cyber defense. Future Generation Computer Systems, 117, 193-204.
14. Alsmirat, M. A., Jararweh, Y., Obaidat, I., & Gupta, B. B. (2017). Internet of surveillance: a cloud supported large-scale wireless surveillance system. The Journal of Supercomputing, 73, 973-992.
15. Bharath G. (2025) Artificial Intelligence Approaches for IoT DDoS Attack Detection, Insights2Techinfo, pp. 1

Cite As

Cajes N. (2025) Predicting and Preventing DDoS Attacks with Deep Learning, Insights2Techinfo, pp.1

816800cookie-checkPredicting and Preventing DDoS Attacks with Deep Learningyes