Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 1361-1387· 0 citations
TL;DR
An enhanced detection framework leveraging deep learning architectures, specifically combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze network traffic patterns, is introduced, demonstrating remarkable capability in identifying zero-day attack variants while maintaining minimal false positive rates.
Abstract
The proliferation of distributed systems has fundamentally transformed how organizations manage their computational infrastructure, yet this advancement has simultaneously exposed critical vulnerabilities to Denial of Service (DoS) attacks. Traditional detection mechanisms struggle to identify sophisticated attack patterns in real-time, particularly within cloud-based and edge computing environments. This research introduces an enhanced detection framework leveraging deep learning architectures, specifically combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze network traffic patterns. Through experimental validation on a dataset comprising 2.3 million network packets collected from enterprise distributed systems, our proposed model achieved a detection accuracy of 98.7%, significantly outperforming conventional machine learning approaches. The system demonstrates remarkable capability in identifying zero-day attack variants while maintaining minimal false positive rates below 1.2%. Implementation across three distinct cloud environments revealed average detection latency of 47 milliseconds, making it viable for real-time deployment. This research contributes to cybersecurity literature by establishing a scalable, adaptive framework that addresses the evolving threat landscape facing distributed computing infrastructure, offering practical implications for system administrators and security professionals managing large-scale networked environments.
Comparative evaluation against existing machine learning and deep learning approaches indicates that the proposed framework achieves competitive accuracy while maintaining deployment-oriented processing speeds, suggesting that the CNN-GRU model is well-suited for SDN security monitoring under controlled experimental conditions.
Victor Anaga, B. Stephen, E. Adediji et al.· E3S Web of Conferences· 0 citations
The findings confirm that the proposed IDSaaS framework provides an efficient, scalable, and adaptive solution for real-time cloud intrusion detection and significantly enhances the reliability and resilience of modern cloud and industrial cybersecurity infrastructures.
Unik B. Lokhande, Kavita Sonawane· Journal of Cloud Computing· 0 citations
A deep learning-based intrusion detection framework that utilizes flow-level statistical features and a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture to capture both spatial and temporal characteristics of network traffic is proposed.
H. Hussein, Saad Raad, Ali Qasim· Al-Noor Journal of Engineeri...· 0 citations
: DDoS attacks have remained very troublesome in terms of the availability and reliability of online services through flooding network infrastructures with malicious traffic. Conventional signature-based and rule-based detection methods are not always useful to deal with the magnitude, variety and dynamism of these attacks. To overcome these problems, this piece of work proposes a deep learning-based real-time detection framework of DDoS attacks in network traffic. The suggested model combines both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks in order to identify discriminative spatial characteristics of traffic patterns and sequence dependencies in the flow of packets, respectively. A hybrid CNN-LSTM model is trained and tested on a publicly available dataset on DDoS traffic and performance measured based on Accuracy, Precision, Recall, F1-score, and ROC-AUC. In order to have transparency, explainable AI (XAI) techniques like SHAP or LIME are used to explain the predictions of the model. To be deployed, the structure includes a Streamlit-based interface that enables real-time traffic simulation, live packet-level prediction, and detection result visualization. The results of the experiments indicate the usefulness of the offered method in attaining impressive detection and providing real-time monitoring of the DDoS threats, which can be applied practically.
D. K., M. K· Proceedings of the 1st Inter...· 0 citations
In rapid development of computer networks, anomaly detection is essential to securing modern communication networks against emerging cyber-attacks, zero-day vulnerabilities, and massive malicious traffic. But conventional intrusion detection systems are plagued by high false alarms, scalability issues and low adaptability to evolving network conditions. To overcome these challenges, this research suggests an adaptive deep learning and ensemble model for scalable real-time detection of network anomalies. This approach combines CNNs for spatial feature learning, RNNs for temporal pattern recognition, and dynamically weighted ensemble learning for making reliable decisions. The model uses normalization, feature selection and class-balancing to enhance generalization. The model is tested across benchmark datasets (CICIDS2017, NSL-KDD, UNSW-NB15) under various attack scenarios. The proposed framework achieves 97.83% accuracy, 97.8% precision, 97.6% recall, 97.7% F1-score and surpasses traditional machine learning and pure deep learning approaches. Further, the model exhibits low inference times and better adaptability to concept drift. This system can be deployed in enterprise, IoT, and edge-security systems for efficient, scalable, and intelligent threat detection.
Kavali Divya Sree, Y. Vishnu Tej· International Conference Com...· 0 citations
