Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India.
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam 602105, India.
Sensors (Basel). 2022 Jul 2;22(13):5006. doi: 10.3390/s22135006.
Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different needs and different scenarios. Many approaches have been followed, both before and after the introduction of machine and deep learning algorithms as intelligence computation. The network traffic analysis is the process of incarcerating traffic of a network and observing it deeply to predict what the manifestation in traffic of the network is. To enhance the quality of service (QoS) of a network, it is important to estimate the network traffic and analyze its accuracy and precision, as well as the false positive and negative rates, with suitable algorithms. This proposed work is coining a new method using an enhanced deep reinforcement learning (EDRL) algorithm to improve network traffic analysis and prediction. The importance of this proposed work is to contribute towards intelligence-based network traffic prediction and solve network management issues. An experiment was carried out to check the accuracy and precision, as well as the false positive and negative parameters with EDRL. Also, convolutional neural network (CNN) machines and deep learning algorithms have been used to predict the different types of network traffic, which are labeled text-based, video-based, and unencrypted and encrypted data traffic. The EDRL algorithm has outperformed with mean Accuracy (97.20%), mean Precision (97.343%), mean false positive (2.657%) and mean false negative (2.527%) than the CNN algorithm.
随着各种应用程序的扩展网络，网络数据流量不断增加，文本、图像、音频和视频是不可避免的需求。网络流量模式识别和数据内容流量分析对于不同的需求和不同的场景至关重要。在引入机器和深度学习算法作为智能计算之前和之后，已经采用了许多方法。网络流量分析是对网络流量进行监禁并深入观察以预测网络流量表现的过程。为了提高网络的服务质量 (QoS)，重要的是使用合适的算法估计网络流量并分析其准确性和精度，以及误报率和误报率。这项拟议的工作提出了一种使用增强型深度强化学习 (EDRL) 算法来改进网络流量分析和预测的新方法。这项工作的重要性在于为基于智能的网络流量预测做出贡献，并解决网络管理问题。进行了实验以检查 EDRL 的准确性、精度以及误报率和误报率参数。此外，还使用卷积神经网络 (CNN) 机器和深度学习算法来预测不同类型的网络流量，这些流量标记为基于文本、基于视频以及未加密和加密数据流量。与 CNN 算法相比，EDRL 算法的平均准确率 (97.20%)、平均精度 (97.343%)、平均误报率 (2.657%) 和平均误报率 (2.527%) 更高。
