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@article{168443,
        author = {Katikam Mahesh},
        title = {Deep learning based Cyber Attacks Detection using Hybrid CNN-AE Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {894-897},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168443},
        abstract = {Thanks to the internet and an extensive number of digital devices, life is quite comfortable these days. As with all excellent there are disadvantages, and the digital world of today is certainly not an exemption. While the internet has transformed our lives in the present, securing personal data remains an immense task. It is it that leads to cyber-attacks The detection of intrusions is key Conventional methods like denial-of-service attacks, phishing scams, and malicious software attacks are poorly detected by techniques like DT, SVM, and ANN. This study presents a unique CNN-Autoencoders Model to automatically improve detection attacks using the publicly available UNSW NB-15 input dataset, thereby enhancing accuracy.},
        keywords = {Classification, Detection, Cyber Attacs, Convolution Neural Networks [CNN], Auto Encoders [AE], denial-of-service [DoS].},
        month = {October},
        }