# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
### Research Focus and Model Contribution  
This paper presents a lightweight, modular deep-learning solution, the EffiGRU-GhostNet model, for identifying DDoS attacks at the network and transport layers. When spotting DDoS patterns at these layers--where large DDoS attacks often use IP, port, and packet-level characteristics to overload network resources and impair connectivity--EffiGRU-GhostNet shines. Maintaining connectivity and service continuity depends on effectively detecting NTL DDoS attacks, especially in environments like IoT networks and critical infrastructure for which our model is primarily intended.  
Our method starts with careful data preparation to guarantee dataset integrity using data normalization, outlier detection, and resolution of missing values. Next, we extract pertinent features using feature engineering, and then dimensionality reduction with PCA-LLP improves computing efficiency. EffiGRU-GhostNet uses these improved datasets to detect intricate network traffic patterns connected with DDoS attacks. We thoroughly evaluate the model's efficacy using the IoT-23 and APA-DDoS datasets. Our all-encompassing strategy offers a dependable and flexible way to reduce NTL DDoS threats, supporting better cybersecurity in IoT contexts.  
To achieve high detection accuracy, minimize false positives, ensure real-time processing capabilities, and handle large-scale datasets, this work seeks to solve significant issues in DDoS assault detection. We used many measures, such as accuracy, recall, F1-score, false positive rate, execution time, and throughput, to assess how well the suggested model dealt with these problems. The model's scalability, efficiency, and dependability are all evaluated by these criteria. This study makes the following key contributions:  
1. Novel PCA-LLP for Dimensionality Reduction: This paper presents PCA-LLP, an innovative dimensionality reduction method developed for the IoT environment. PCA-LLP significantly improves the efficiency and scalability of DDoS detection systems by efficiently reducing the computational complexity of IoT datasets while maintaining critical information.
2. Innovation of EffiGRU-GhostNet Model: We present EffiGRU-GhostNet, a lightweight and new deep modular architecture for DDoS detection in IoT systems. This model combines upgraded EfficientNet with GRU (Gated Recurrent Unit), Dense, and GhostNet Module embedded layers to provide higher performance in collecting sophisticated network traffic patterns. EffiGRU-GhostNet maintains a balance between computational complexity and detection accuracy, meeting the resource limits of IoT devices while retaining powerful detection capabilities.
3. Achievement of low computational complexity with high accuracy: Our technique achieves minimal computing complexity while maintaining detection accuracy, making it appropriate for resource-constrained IoT contexts. The proposed method performs better on real-world datasets, such as IoT-23 and APA-DDoS, by improving model design and employing lightweight approaches like GhostNet. Rapid and reliable attack mitigation with reduced computational overhead is essential for Internet of Things (IoT) devices, and our study highlights the need for fast and effective DDoS detection techniques for these systems.
4. Low false positive rate: Our proposed technique has a low false positive rate, demonstrating its ability to discern between legitimate network activity and DDoSA. This functionality improves the reliability of DDoS detection systems by minimizing false alarms and allowing for more accurate threat identification and response in IoT contexts.  
Section 2 presents the pertinent research, whereas Section 3 presents the proposed model. The paper is concluded in Section 5, and the simulation results are presented in Section 4.