A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm

Abstract

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.

MD-MARS: Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network

“…When accuracy is compared with existing techniques such as random forest, weka lazy model, conditional inference tree, ensemble model with equal weight [12], KNN [58], Bayesian [59], Ensemble Classifier [60] and enhanced deep reinforcement learning [61], it will show that the proposed framework outperforms well shown in TABLE 8. FIGURE 11 shows the scatter plot relation between the actual and predicted MARS model with and without tuning parameters.…”

PatchRLNet: A Framework Combining a Vision Transformer and Reinforcement Learning for The Separation of a PTFE Emulsion and Paraffin

“…Reinforcement learning has been demonstrated as one of the most effective methods for achieving optimal and efficient policy selection [13,14]. By leveraging the interaction between an agent and its environment, reinforcement learning can formulate optimal strategies based on environmental feedback [15][16][17]. In this study, we establish a reinforcement learning framework with the vision transformer classification model serving as the environment.…”

Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis

“…The goal of the subset of traffic analysis methodologies, known as traffic classification, is to categorize traffic flow into several predetermined groups, such as normal or abnormal traffic and the application type [1]. It makes it easier for Internet service providers to manage their infrastructures effectively and meets the requirements for quality of service [2][3][4]. The first traffic processors used each application's port number [3] to determine who it was.…”

“…By examining the packet header, this approach exclusively reveals the port numbers and their correspondences. Due to this, the method that looks at port numbers turned out to be the fastest and easiest [4]. However, there are some problems with this plan.…”