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@article{171278,
        author = {K.Abarna and B.Manivannan and D.Radhika},
        title = {A Survey Paper on Automated Intrusion detection using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3482-3486},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171278},
        abstract = {The rapid evolution of cyber threats necessitates the development of robust and adaptive intrusion detection systems (IDS) to safeguard critical networks and systems. Deep learning (DL) has emerged as a powerful tool in automating intrusion detection, offering superior capabilities in feature extraction, anomaly detection, and real-time threat identification. This survey paper comprehensively reviews the state-of-the-art research on automated intrusion detection leveraging deep learning techniques. It explores various architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and auto encoders, highlighting their strengths and limitations in addressing different intrusion detection challenges. The paper also examines hybrid approaches that combine DL with traditional methods to enhance detection accuracy and mitigate false positives. Emphasis is placed on the performance evaluation of these techniques using benchmark datasets, their adaptability to evolving threats, and their deployment in real-world scenarios. This reviews significant contributions from recent studies, focusing on the application of various deep learning models for automated intrusion detection.},
        keywords = {Intrusion Detection Systems (IDS), Deep Learning (DL), Cybersecurity, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN).},
        month = {December},
        }