Deep Neural Network Model for Improved DDoS Attack Detection in Cloud Environments

As the prevalence of Distributed Denial of Service (DDoS) attacks continues to escalate, safeguarding cloud environments against these threats becomes paramount. This paper introduces a Deep Neural Network (DNN) model designed to enhance the accuracy and efficiency of DDoS attack detection in cloud environments. Leveraging the inherent capabilities of deep learning, the proposed model exhibits improved performance on the widely recognized NSL-KDD dataset. The research findings demonstrate a substantial increase in accuracy, underscoring the efficacy of the DNN model in fortifying the security posture of cloud infrastructures. The escalating frequency and sophistication of Distributed Denial of Service (DDoS) attacks pose a substantial threat to the security of cloud environments. In response to this pressing concern, this paper introduces a Deep Neural Network (DNN) model engineered to significantly enhance the accuracy and efficiency of DDoS attack detection in cloud infrastructures. By harnessing the inherent capabilities of deep learning, the proposed model represents a breakthrough in fortifying the security posture of cloud systems. This research employs the widely recognized NSL-KDD dataset, a comprehensive resource for Intrusion Detection System (IDS) evaluation, to evaluate the model's performance. The proposed DNN model transcends conventional methods by autonomously learning intricate patterns within network traffic data, adapting to the evolving landscape of DDoS attacks. The literature survey delves into the vulnerabilities associated with DDoS attacks in cloud environments, emphasizing the need for innovative detection mechanisms. Previous research has underscored the effectiveness of deep learning, particularly DNNs, in addressing complex cybersecurity challenges, positioning them as ideal candidates for enhancing threat detection capabilities.