By: Nicko Cajes; Northern Bukidnon State College, Philippines
ABSTRACT
This article discusses the impact of DDoS attacks and highlights the importance of developing an effective defense mechanism against DDoS attacks. By using hybrid deep learning methods to predict, mitigate, and prevent DDoS attacks, the limitation of traditional security systems is solved because using this method can provide real-time detection and fast adapting to the new DDoS attack pattern.
INTRODUCTION
What do you think will happen, if one of the famous online shopping platforms like Shopee stops its service? A lot will be frustrated, several business owners will lose a lot of money, delivery riders will lose jobs, and customers will give a lot of negative feedback towards Shopee. Those were a few things that would happen if their service was stopped. This is the dark reality of Distributed Denial of Service (DDoS) attacks, and it is hard to avoid if a business doesn’t pay enough attention to its security mechanism. With the rising number of records between the first quarter and third quarter of 2024, which reached up to 2,200 DDoS attacks every hour [1], the need for a solution to this problem is now a must. But, in the research field, researchers didn’t get this thing out of hand before making a strategic solution to counter it, by utilizing technology, a lot of them found and used a hybrid deep learning approach to counter the critical threat that DDoS provides. This article will discuss how to be safe against DDoS attacks using advanced defense mechanisms.

UNDERSTANDING DDOS ATTACK
The primary aim of a DDOS attack is to shut down the target server by sending a flood of requests, making the server unable to respond to legitimate requests made by the user. One of the Cloudflare reports [2] shows that in quarter 3 of 2022, there was the largest recorded DDoS attack tracked, it registers a throughput of 2.5 Tbps (Terabits per second) and uses Mirai botnet variants to target Wynncraft servers. This event highlights the critical need to create an effective defense mechanism against the dynamically evolving attack techniques of DDoS attackers. According to the report by [3], the expected cybercrime cost will grow 15% every year in the following 5 years, and it will reach up to $10.5 Trillion annually by 2025, making a huge leap from $3 Trillion in 2015. These huge numbers are a testament that even with the huge effort made by the experts in this field, it is still not enough to prevent or even mitigate the rising number of effects it gives.

TRADITIONAL DEFENSE MECHANISM
In the past years, traditional defense mechanisms have been used a lot like Firewalls, Intrusion Detection Systems, and other software-embedded security. These security mechanisms have been widely adapted to devices and were the most used defense mechanism at the present time.
Firewall: In the early 1980s, the first generation of firewalls were implemented functioning with the use of a set of basic rules that regulates the network between the local and the internet [4]. A Firewall is an important tool that provides fundamental security to networks and interconnected devices, which mainly functions on the network layer of the Open System Interconnection (OSI) model.
Intrusion Detection System (IDS): Intrusion detection is the process of keeping an eye on things that happen in a computer system or network and examining them to look for indications of potentially dangerous issues. Examples are the threats, violations of use guidelines or conventional security procedures, and hacking attempts. This security system primarily focuses on identifying possible malicious activities, documenting those activities, and informing security administrators of the information that is recorded [5].

Firewalls and IDS are a good example of a security system in a digital network. However, these security systems somehow faced a lot of challenges detecting the new sophisticated cyber attacks generated by the attackers. One of the limitations or disadvantages of these systems is their static nature, meaning they can’t quickly adapt to the rapidly evolving attack techniques made by malicious actors, highlighting their vulnerabilities and making them easily penetrated and target of attack.
HYBRID DEEP LEARNING FOR DDOS DEFENSE
With the advent of technology, innovative solutions emerged to solve DDoS attack problems like hybrid deep learning techniques, and it became the bright spot that was found by the researchers. In using a hybrid deep learning approach, developers somehow follow a common phase to build an effective security system. And these are the selection of dataset, preprocessing of dataset, model selection, model training and optimization, and the evaluation of the model and performance matrix.
Dataset Selection: In the phase of dataset selection developers select a specific dataset that strongly aligns to their cause, for example if the researcher aims to develop a hybrid deep learning model related to prevention of DDoS attack, they can select an appropriate dataset that is reliable and has a huge amount of data where their model will train from.
Data Preprocessing: In this phase the process of data cleaning, identifying noise, balancing the data, and removing unnecessary features for a balance dataset is done to have accurate data for the model to train from.
Model Selection: In this phase the selection of hybrid model, machine learning, or deep learning models are selected, for example the model like CNN-GRU or CNN-LSTM model are among the choices which developers can choose.
Model Training and Optimization: In this phase developer trains the model using the preprocessed and labeled data. in this phase they are going to adjust weights and biases through optimization techniques like back propagation and gradient descent.
Evaluation and Performance Matrix: In this phase developer evaluates the model’s performance with the use of common evaluation metrics to see if it is good and if the result doesn’t meet the requirements, they usually do another round of testing with some enhancement.

Unlike the traditional security system, the advanced security system which applies hybrid deep learning techniques has so many advancements that compensate for the lack of ability of the traditional security systems. By combining the ability of Machine Learning and Neural Networks, prediction, mitigation, and prevention of DDoS attacks becomes possible. Due to the complexity of solving DDoS attacks together with their dynamically evolving attack techniques and patterns, the necessity of developing an innovative solution is at a high stake. With that, hybrid deep learning techniques, through the study and experiment conducted by the researchers, show huge potential in solving this problem.
CONCLUSION
The threats online are hard to prevent, especially in today’s world where everyone is connected and uses the internet. Even with the huge contribution of traditional security systems to defend against DDoS attacks, their limitation remains one of their vulnerabilities in detecting new attacks. But, with the rise of hybrid deep learning techniques, the problem faced by traditional security systems was resolved. Together with the detection of DDoS attacks in real-time, the said attack can also be avoided due to its ability to predict attack patterns by the advanced security systems. In the provided solution and efficiency, the question has shifted from the effectiveness of the security system to the ability of the organization or companies to quickly adapt to this technology to be safe from DDoS attackers. By helping each other, solving this problem is highly possible. The prevention of this is the dream of many, as this will enable us to have a future where we don’t need to be worried about the accessibility of important services online.
References
- Joshi, S. (n.d.). 45+ DDoS attack statistics: Key data and takeaways for 2025. https://learn.g2.com/ddos-attack-statistics
- Cloudflare DDoS threat report 2022 Q3. (2024, October 9). The Cloudflare Blog. https://blog.cloudflare.com/cloudflare-ddos-threat-report-2022-q3/
- How are DDoS attacks impacting business and services. (2022, March 26). Cyber Magazine. https://cybermagazine.com/cyber-security/how-are-ddos-attacks-impacting-businesses-and-services
- https://ieeexplore.ieee.org/abstract/document/9720435
- https://ieeexplore.ieee.org/abstract/document/9620099
- Hammad, M., Abd El-Latif, A. A., Hussain, A., Abd El-Samie, F. E., Gupta, B. B., Ugail, H., & Sedik, A. (2022). Deep learning models for arrhythmia detection in IoT healthcare applications. Computers and Electrical Engineering, 100, 108011.
- Chui, K. T., Gupta, B. B., & Vasant, P. (2021). A genetic algorithm optimized RNN-LSTM model for remaining useful life prediction of turbofan engine. Electronics, 10(3), 285.
- Nawal Kishor (2021) Operating System Security and Significance of Logging, Insights2Techinfo, pp.1
Cite As
Cajes N. (2025) DDoS Resilience: Building a Safer Digital Landscape with Hybrid Deep Learning, Insights2Techinfo, pp.1