A network packet identification and classification is a fundamental requirement of network management to maintain the quality of service, quality of experience, efficient bandwidth utilization, etc. This becomes increasingly significant in light of the Internet’s and online applications’ rapid expansion. With the advent of secure applications, more and more encrypted traffic is proliferated on the internet. Specifically, peer-to-peer applications with user-defined protocols severely affect network management. So there is necessary to identify and classify encrypted traffic in a network. To overcome this, our proposed approach network-packet binary classification is implemented to classify the network traffic as encrypted or compressed packets, with better classification accuracy and with the usage of a limited amount of classification time. To achieve this, our model uses a Decision tree classifier with one of the efficient feature selection methods, Autoencoder. Our experimental results show that our model outperforms the most state-of-the-art methods in terms of classification accuracy. Our model achieved 100% classification accuracy within 0.009 s of processing time.