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@article{203411,
        author = {Hrushikesh R Pujar and Dr TNR Kumar},
        title = {Classification and Detection of Network Attacks in Encrypted Traffic Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11984-11989},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203411},
        abstract = {It is common for wireless networks to use encryption protocols like WPA or WPA2 in order to provide secured communication. However, while encryption offers protection for the users' data, it reduces the capability of the traditional IDS to perform inspection of packets' payload.
In the present research, the Wi-Fi traffic, which is encrypted, is analyzed by considering the statistical and flow features alone. The trilayer neural network is developed with the help of MATLAB function fitcnet, and the traffic is classified into various attack classes. The neural network is trained by considering 13 statistical features and employing fivefold cross-validation methodology. As the basis, the logistic regression and ensemble learning models are taken into account.
It can be seen that the neural network model provides an accuracy of 99.9%, whereas the logistic regression and ensemble learning models provide accuracies of 88.46% and 99.60%, respectively.},
        keywords = {Encrypted Traffic, Wi-Fi Security, Neural Network, Machine Learning, Intrusion Detection.},
        month = {May},
        }