By: Gonipalli Bharath Vel Tech University, Chennai, India International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan, Gmail: gonipallibharath@gmail.com
Abstract:
Distributed Denial of Service (DDoS) attacks are one of the biggest threats to modern digital systems, especially for IoT-based systems. Traditional security mechanisms are often unable to identify and counter DDoS attacks in real-time due to the dynamic and complicated nature of cyber-attacks. In this article, the role of hybrid deep learning frameworks in enhancing DDoS attack mitigation is explored. By including more than one deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), the proposed method improves response time and detection accuracy. Hybrid framework architecture, advantages over separate models, and the impact on real-time network security are discussed here. From the results, we can observe that hybrid deep learning frameworks give enhanced performance in the detection and prevention of DDoS attacks than the traditional methods.
Introduction:
DDoS attacks disrupt online operations by overwhelming network resources with malicious traffic. New DDoS attacks have made it difficult to detect using the rule-based and signature-based systems, hence not being as effective[1]. Machine learning and deep learning have become novel opportunities in security that offer responsive and intelligent methods of threat detection. However, standalone deep learning models lack functionality in restricting false positives and responding to dynamic patterns of attacks. A hybrid deep learning model combines the strengths of two or more models and is more accurate and effective in detecting and preventing DDoS attacks.
Hybrid Deep Learning Framework for DDoS Mitigation:
Hybrid deep learning framework leverages multiple architectures of neural networks to improve the detection of threats. The idea is to have different models blend together to complement each other’s deficiencies and gain better performance overall. The proposed framework comprises the following components:
Fig (i): Hybrid Deep Learning Framework for DDoS Attack Mitigation
Data Preprocessing and Feature Extraction:
Traffic generated by a DDoS attack contains potentially malicious patterns that are detected with the help of proper feature engineering[2]. Network traffic data is utilized within the model for detecting significant features such as packet rate, source IP trends, payload, and anomaly indication features. Principal Component Analysis (PCA) and Autoencoders are used for selecting features that enhance input data for the purpose of better model performance[3].
LSTM-Based Traffic Pattern Analysis:
LSTM networks are highly capable of dealing with sequential data, and thus they are most suited to traffic pattern analysis using time-series network traffic[4]. The LSTM model can recognize time-based anomalies in traffic patterns and also identify probable attack signatures. From learning historical traffic, the model is able to predict anomalies precisely[5].
CNN for Feature Learning and Classification:
CNNs are integrated into the framework to enhance feature learning and classification. In contrast to independent machine learning models, CNNs learn important features from raw traffic data autonomously without any manual feature engineering[6]. This increases the capacity to identify subtle attack patterns that would be lost in individual models.
Hybrid Model Fusion and Decision Making:
LSTM and CNN models’ outputs are combined using a decision fusion approach, such as weighted average or ensemble learning algorithms. This will make the final decision depend on both spatial characteristics and temporal trends, thus an enhanced detection process[7]. The hybrid model reduces false positives and real-time DDoS attack blocking.
Advantages of Hybrid Deep Learning Frameworks:
- Improved Detection Accuracy: By using more than one deep learning model, the framework reduces misclassification errors and improves detection accuracy[8].
- Ability to Adapt to Evolving Threats: Hybrid models have the ability to adapt to new attack vectors with the aid of continuous learning from network traffic patterns.
- Real-Time Evasion: The integration of deep learning with automated response systems makes real-time detection and evasions of DDoS attacks possible.
- Reduce False Positives: Consolidating different detection techniques lowers false alarms, which improves system dependability.
Conclusion:
Hybrid deep models present a promising solution for maximizing DDoS attack mitigation in modern network environments. Through the integration of LSTM and CNN models, the proposed method increases detection precision and real-time cyber threat response. Optimizing computational complexity and exploring other deep models can be focused in future work. Since cyber threats become more sophisticated over time, hybrid deep models present a scalable and dynamic solution for safeguarding digital infrastructures against DDoS attacks.
References:
- S. Pasupathi, R. Kumar, and L. K. Pavithra, “Proactive DDoS detection: integrating packet marking, traffic analysis, and machine learning for enhanced network security,” Clust. Comput., vol. 28, no. 3, p. 210, Jan. 2025, doi: 10.1007/s10586-024-04849-x.
- M. S. E. Sayed, N.-A. Le-Khac, M. A. Azer, and A. D. Jurcut, “A Flow-Based Anomaly Detection Approach With Feature Selection Method Against DDoS Attacks in SDNs,” IEEE Trans. Cogn. Commun. Netw., vol. 8, no. 4, pp. 1862–1880, Dec. 2022, doi: 10.1109/TCCN.2022.3186331.
- E. Odhiambo Omuya, G. Onyango Okeyo, and M. Waema Kimwele, “Feature Selection for Classification using Principal Component Analysis and Information Gain,” Expert Syst. Appl., vol. 174, p. 114765, Jul. 2021, doi: 10.1016/j.eswa.2021.114765.
- J. Bi, X. Zhang, H. Yuan, J. Zhang, and M. Zhou, “A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 3, pp. 1869–1879, Jul. 2022, doi: 10.1109/TASE.2021.3077537.
- M. A. Salahuddin, V. Pourahmadi, H. A. Alameddine, Md. F. Bari, and R. Boutaba, “Chronos: DDoS Attack Detection Using Time-Based Autoencoder,” IEEE Trans. Netw. Serv. Manag., vol. 19, no. 1, pp. 627–641, Mar. 2022, doi: 10.1109/TNSM.2021.3088326.
- D. Alghazzawi, O. Bamasag, H. Ullah, and M. Z. Asghar, “Efficient Detection of DDoS Attacks Using a Hybrid Deep Learning Model with Improved Feature Selection,” Appl. Sci., vol. 11, no. 24, Art. no. 24, Jan. 2021, doi: 10.3390/app112411634.
- D. Alghazzawi, O. Bamasag, H. Ullah, and M. Z. Asghar, “Efficient Detection of DDoS Attacks Using a Hybrid Deep Learning Model with Improved Feature Selection,” Appl. Sci., vol. 11, no. 24, Art. no. 24, Jan. 2021, doi: 10.3390/app112411634.
- M. Sajid et al., “Enhancing intrusion detection: a hybrid machine and deep learning approach,” J. Cloud Comput., vol. 13, no. 1, p. 123, Jul. 2024, doi: 10.1186/s13677-024-00685-x.
- Alweshah, M., Khalaileh, S. A., Gupta, B. B., Almomani, A., Hammouri, A. I., & Al-Betar, M. A. (2022). The monarch butterfly optimization algorithm for solving feature selection problems. Neural Computing and Applications, 1-15.
- Gupta, B. B., Joshi, R. C., & Misra, M. (2009). Defending against distributed denial of service attacks: issues and challenges. Information Security Journal: A Global Perspective, 18(5), 224-247.
- Dhananjay Singh (2021) Captcha Improvement: Security from DDoS Attack, Insights2Techinfo, pp.1
Cite As
Bharath G. (2025) Enhancing DDoS Attack Mitigation Using Hybrid Deep Learning Frameworks, Insights2Techinfo, pp.1