Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{161639,
        author = {Anisha L and J. Caroline Misbha},
        title = {EFFECTIVE PREDICTION OF DDOS ATTACK USING DEEP  LEARNING CLASSIFIERS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {5},
        pages = {246-250},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161639},
        abstract = {Distributed Denial of Service (DDoS) assaults are a frequent moniker for distributed network attacks. These attacks take use of certain restrictions that apply to each asset of the arrangement, such as the design of the website for the allowed organisation. In this research, a deep learning method for anticipating DDoS attacks is proposed. The techniques of deep learning were created for this project's classification of DDOS assaults. The deep learning methodology includes the classification algorithms Multilayer Perceptron (MLP) and Long Short-Term Memory Networks (LSTM). The datasets are pre-processed using Standard Scaler. To enable the identification and categorization of DDOS assaults, deep learning methods are deployed. An artificial neural network feed-forward model called MLP converts input sets into output sets. In order to diagnose defects in that area, the LSTM classifier is built to categorize errors according to their nature. This suggested project generated a confusion matrix in order to evaluate the model's performance. Python software is used to implement this simulation.},
        keywords = {DDOS, LSTM, MLP, DL, Confusion matrix.},
        month = {},
        }