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@article{200937,
        author = {Lakshmidevi B and Chandru M and Nithish Kumar J and Selva Kumar.S},
        title = {Hybrid CNN-RNN Framework for Detecting Complex and Zero-Day Cyber Attacks Using Anomaly Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {62-67},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200937},
        abstract = {The rapid growth of network systems has led to an increase in cyber-attacks, making network security a major concern. Traditional machine learning methods are not efficient in detecting complex and zero-day attacks. This project proposes a hybrid CNN-RNN framework for anomaly detection in network traffic data. The system uses Convolutional Neural Networks (CNN) to extract spatial features and identify hidden patterns. It uses Recurrent Neural Networks (RNN) to analyze temporal behavior and sequence patterns. The combination of CNN and RNN improves the overall detection performance. The system can detect both known and unknown cyber-attacks effectively. Data preprocessing techniques are applied to clean and prepare the dataset. Feature extraction helps in selecting important information for better accuracy. The model is trained using deep learning techniques for intelligent decision making. The system reduces false positive rates compared to traditional methods. It provides fast and efficient detection suitable for real-time applications. The proposed framework is scalable and adaptable to dynamic network environments. It enhances overall cybersecurity by providing reliable results.},
        keywords = {.},
        month = {May},
        }