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@article{196441,
        author = {Tirumali Sri Tejaswini and Tirumala Kamala Shreya and Vellanki Pranitha and Dr. R. Manivannan},
        title = {Anomaly Detection In Network Traffic Using Machine Learning and Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2729-2732},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196441},
        abstract = {Network security has become an essential issue with the rapid development of internet communication and the emergence of cyber-attacks. This paper proposes a system that can detect anomalies in the network traffic using the NSL-KDD dataset with the help of machine and deep learning algorithms. The authors have used various preprocessing steps such as categorical encoding, data cleaning, and feature scaling in the proposed system. The authors have used an ensemble method with four algorithms Support Vector Machine, Random Forest, Deep Neural Network, and Extreme Learning Machine to detect anomalies in the network traffic. The performance of the proposed system is evaluated using various parameters such as accuracy, precision, recall, and confusion matrix. The results show that the proposed system can detect malicious patterns in the network traffic and improve security.},
        keywords = {Network Security, Anomaly Detection, Machine Learning, Deep Learning, NSL-KDD Dataset.},
        month = {April},
        }