Cybersecurity Threat Detection Using OpCyNet and DBRA: A Deep Learning Approach for DDoS Attack Mitigation on CICDDoS2019
Srinivas Cheekati, Chandrakanth Reddy Borra, Piyush Kumar Pareek, Ramya Vani Rayala, S. Sri Nandhini Kowsalya, Janani Selvam · 2025
Threatening services by overwhelming targeted systems with traffic from numerous sources, distributed denial-of-service (DDoS) assaults are a major concern for computer networks and systems. It is now an essential cybersecurity duty to detect these threats in real-time. Unfortunately, present approaches for detecting DDoS attacks have a hard time capturing the intricate patterns of attack traffic and have a high false positive rate. This research proposes an enhanced approach for detecting DDoS attacks using an Optimized Cybernet Model (OpCyNet), a deep learning framework integrated with the Developed Battle Royale Optimization Algorithm (DBRA) using CICDDoS2019 dataset. In order to extract, improve, and classify characteristics of network traffic, the OpCyNet model has eleven learnable layers. These layers include three fully connected (FC) layers and eight convolutional layers. For better generalization and to avoid overfitting, the model uses dropout layers, max-pooling, batch normalization, leaky ReLU activation, and others. Additionally, cross-channel normalization and softmax classification enhance the model's ability to distinguish between normal and malicious network traffic patterns. The DBRA metaheuristic algorithm is used to further optimize feature selection and classification, which greatly reduces computational cost while keeping detection accuracy high. Experimental results on the CICDDoS2019 dataset demonstrate that the OpCyNet-DBRA model outperforms conventional deep learning and machine learning approaches in terms of accuracy, precision, recall, F1-score, and false positive rate (FPR). When applied to real-world network settings, the suggested framework improves threat detection and mitigation while being efficient, scalable, and intelligent. To enhance cybersecurity resilience in the face of ever-changing distributed denial-of-service (DDoS) attacks, this paper proposes a method for cyberattack detection that combines deep learning with metaheuristics. To further improve the proposed system's effectiveness, future work will center on real-time deployment, adversarial attack resistance, and cross-dataset validation..