Enhancing Cyber Security to Predict DDOS Attack Using Deep Learning Algorithms

Abstract

Artificial Intelligence (AI) is a technology that allows machines to think andlearn like humans. It can perform tasks like human intelligence, such as understandinglanguage, recognizing images, solving problems, and making decisions. In our existing system, they proposed a new approach detect DDOS attacks by using Artificial Intelligence concepts. Such as Convolutional Neural Networks (CNNs) combined with adaptive architectures and transfer learning techniques At Particularly, they use the Convolutional Neural Networks (CNNs) to shown promise in detecting and mitigating DDOS attacks by automatically learning feature representations and identifying complex patterns in network traffic data. Even though the existing work is effective, there are still some issues such as High Computational Cost, Struggling with Large Datasets, Time Consuming Training, Limited Use for Other cyber threats. For these issues, we proposed to find solutions to develop a hybrid model that combines multiple algorithms or techniques to enhance the prediction accuracy of DDOS attack detection system. We use cloud resources that can reduce costs and improve processing speed and we will use deep learning models effectively to handle large datasets, for time consuming we planned to split the work across several computers so that the training happens faster. Using multi-task learning, combining models and focusing on common features can help the model to detect a wider range of threats.

Introduction

DDoS Attacks:
Distributed Denial of Service (DDoS) attacks disrupt servers or networks by flooding them with excessive traffic from multiple compromised devices (botnets), causing service inaccessibility. They differ from DoS attacks, which come from a single source. DDoS attacks are classified into volume-based, protocol, and application-layer attacks, using techniques like UDP floods, SYN floods, and DNS amplification. Mitigation strategies include firewalls, rate limiting, CDNs, load balancers, and specialized protection services. Effective defense is crucial to avoid financial loss and reputational damage.

GRU (Gated Recurrent Unit):
GRU is a simplified recurrent neural network designed to efficiently process sequential data by using two gates—reset and update—to control memory without the complexity of LSTM. It balances computational efficiency and predictive power, making it suitable for tasks like NLP, speech recognition, and time-series forecasting, especially in resource-constrained environments.

LSTM (Long Short-Term Memory):
LSTM is a more complex RNN architecture that uses memory cells and three gates—input, forget, and output—to capture long-term dependencies in sequences. It is effective for complex sequential tasks such as NLP and speech recognition but requires more computational resources than GRU.

Literature Survey on DDoS Detection/Mitigation:

• SDN-based dynamic architectures using machine learning can detect low-rate DDoS with high accuracy.

• Blockchain combined with SDN and smart contracts enable intra- and inter-domain collaborative DDoS mitigation.

• Matching Pursuit algorithms improve detection of low-density resource depletion DDoS attacks with high true positive rates.

• Improved KNN algorithms using the concept of attack degree enhance detection accuracy in SDN.

• Statistical models like Rhythm Matrix effectively detect application-layer DDoS attacks by analyzing user behavior patterns.

Architectural Diagrams:
These visualize the system’s component arrangement and interactions, aiding understanding, collaboration, and informed decision-making during development.

Proposed System:
A hybrid deep learning model combining GRU and LSTM networks is proposed for enhanced DDoS detection in Software-Defined Networking (SDN). This model leverages GRU’s efficiency and LSTM’s ability to capture long-term dependencies, trained on the CICDDoS2019 dataset, achieving improved accuracy and adaptability. It supports real-time monitoring and identification of sophisticated, multi-vector DDoS attacks.

Detailed GRU Components:

• Reset Gate: Controls how much past information is forgotten.

• Update Gate: Balances retention of past and incorporation of new data.

• Final Hidden State: Combines past and new information based on update gate.

Detailed LSTM Components:

• Forget Gate: Decides what previous information to discard to manage memory efficiently.

• Final Hidden State: Determined by the output gate, representing processed information at the last time step.

Conclusion

The rise in Distributed Denial of Service (DDoS) attacks poses a severe threat to Software-Defined Networking (SDN), making traditional Machine Learning (ML) models ineffective due to their inability to adapt to evolving attack patterns. These models often struggle with outdated training data, high false-positive rates, and limited scalability, making zero-day attack detection challenging. To overcome these limitations, a hybrid deep learning approach integrating Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks is proposed. By combining GRU’s efficiency in handling sequential data with LSTM’s ability to capture long-term dependencies, the model enhances feature extraction and effectively detects complex attack patterns. Training the model on the CICDDoS2019 dataset ensures higher accuracy, improved adaptability, and reduced false alarms, making it more suitable for SDN environments. This hybrid approach significantly strengthens intrusion detection systems (IDS) by ensuring network stability and security. Future improvements may include reinforcement learning for adaptive attack prevention, better model scalability, and enhanced real-time mitigation mechanisms. By leveraging GRU and LSTM’s strengths, the system provides a robust and computationally efficient solution to counter DDoS attacks, ensuring a resilient cybersecurity framework against evolving threats. Further research is needed to enhance the model\'s adaptability to emerging attack patterns, ensuring its effectiveness against evolving cybersecurity threats. Exploring the integration of advanced real-time monitoring techniques and adaptive learning mechanisms can contribute to the continuous improvement of the algorithm\'s responsiveness. Additionally, efforts should be directed toward scalability, enabling the algorithm to handle large-scale network environments efficiently. Collaboration with cybersecurity experts and industry practitioners can provide valuable insights for practical implementation and validation of the hybrid model in diverse network settings.

References

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Copyright

Copyright © 2025 Dr. R. G. Suresh Kumar, Dr. S. Udhayashree, A. Sathiya, K. Subashshree, P. Abirami. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.