Hybrid Deep Learning Models for DDoS Attack Detection: A Comparative Study

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 have become the greatest security threat within the IoT network, leading to network availability loss and financial as well as operational losses. Traditional detection methods struggle to adapt with evolving attack patterns, and this has led to the application of deep learning algorithms. This work investigates hybrid deep learning models, which combine multiple architectures, such as LSTM-CNN, BiLSTM-GRU, and Transformer-based models, to enhance detection accuracy and adaptability. A comparative analysis is done keeping in view detection performance, computational complexity, and real-time feasibility. The findings conclude that hybrid models are better than standalone deep learning models because they effectively detect spatial and temporal patterns of attacks.

Introduction:

IoT devices has witnessed an increase in cyberattacks, the most harmful of which is the DDoS attack. The conventional security models such as rule-based and statistical models fail to detect sophisticated patterns of attacks[1]. Deep learning (DL) has emerged as a suitable solution for the problem, and among the models proven to be efficient in anomaly detection are CNN, LSTM, and GRU. Individual DL models are not as effective in processing complex network traffic data. Deep learning hybrid models aim to do better than this through the strengths that different architectures bring to bear to enhance the detection efficiency and accuracy.

Hybrid Deep Learning Approaches for DDoS Detection:

Hybrid deep learning models use a variety of fundamental architectural concepts to improve their capacity to recognize patterns as well as retrieve features. Some instances of hybrids comprise:

CNN-LSTM Model performs three functions by using Convolutional Neural Networks to extract space-based features and Long Short-Term Memory networks to find temporal patterns. The CNN component detects important spatial dependencies within traffic network regions but LSTM processes the chronological attack patterns effectively[2].

BiLSTM-GRU Model: employs Gated Recurrent Units (GRU) for effective computing and bidirectional learned structures (BiLSTM) to enhance pattern learning. The BiLSTM layer processes both previous and subsequent sequence context which the GRU simplifies training complexity through its streamlined gate mechanisms[3].

The Transformer-Based Model achieves long-range dependence modeling in network traffic data through its self-attention mechanism. The serial nature of LSTM processing sequences is replaced in transformers by parallel computing that enhances the detection speed while scaling up for complex attacks[4].

The Autoencoder-LSTM Model applies the autoencoder for traffic data dimensional reduction before LSTM performs an anomaly detection analysis. The method displays great potential for noise reduction together with enhanced feature selection[5].

In order to identify distributed denial of service (DDoS) assaults in a manner that protects confidentiality, a hybrid federated learning method puts different deep learning algorithms together across distributed systems. The security features are improved through this system alongside decreased dependence on collecting data at central locations[6].

The models efficiently analyze network flow information to identify regular and attacking traffic patterns successfully. The system’s capabilities to adjust the detection of diverse attack patterns make such methods surpass traditional defense approaches.

Hybrid Deep Learning Model:

The high-level operational sequence of a hybrid deep learning-based DDoS detection system follows the pattern described below:

• Data Collection: Gather network traffic data from benchmark datasets like CIC-DDoS2019 and NSL-KDD.
• Preprocessing: Feature extraction combined with normalization as well as encoding functions as the first step for data preparation in the training process.
• Model Selection: The system selects hybrid models which include CNN-LSTM together with the combination of BiLSTM-GRU and Transformers.
• Training and Validation: The system trains its models through data labeling processes which optimize performance during the validation phase.
• Detection Evaluation: The system performs real-time detection by assigning network traffic into two classes: normal traffic or DDoS attack.
• Performance Evaluation: Evaluation according to recognition a period of time accuracy, & F1-score.

Comparative Analysis of Hybrid Models:

Comparative analysis of different hybrid deep learning models is conducted according to the following parameters:

• Detection Accuracy: Defines the effectiveness of the model to detect normal traffic and attack traffic.
• Computational Efficiency: Defines the training duration and resource utilization.
• Real-Time Applicability: Evaluates the deployability in real-time scenarios.

Conclusion:

Hybrid deep learning models are an effective solution to detect DDoS attacks on IoT networks through the utility of different architectures. Comparative studies indicate the superior performance of these models compared to singular methods concerning accuracy as well as real-time performance. Improving transformer-based models for real-time processing and exploring federated learning approaches for decentralized DDoS detection could be the areas of future research.

References:

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Cite As

Bharath G. (2025) Hybrid Deep Learning Models for DDoS Attack Detection: A Comparative Study, Insights2Techinfo, pp.1

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