Analysis of Various Network Traffic Classification Techniques

There are several interconnected entities present within the networked data for which the generation of inferences is important. For instance, hyperlinks are used to interconnect the web pages, calls are used to link the phone accounts, and references are used to connect the research papers and so on. Almost every existing application includes networks within it. The daily lives of individuals include social networking, making financial transactions, generating networks that show physical systems and so on. The manner in which the nodes present within the system influence each other can be known through this research. On the basis of observed attributed of an object within the system, another attributed is predicted using new model. The various network traffic classification techniques are reviewed in terms of certain parameters.

Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN

In the rapid development of network technology, with the improvement of the quality and quantity of network users’ demands, more and more network information technology and excessive network traffic also raise people’s attention to the internal network security. Especially for the classification and resource allocation of encrypted network traffic, the research of related technologies has become the main research direction of the development of network technology. The extensive application of deep learning provides a new idea for the study of traffic classification. Therefore, on the basis of understanding the current situation, the improved convolutional neural network is selected to conduct an in-depth discussion on traffic classification and resource allocation of encrypted networks based on deep learning. The performance of the system is verified from the perspective of practical application.

Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.

Prospects for Generative - Adversarial Networks in Network Traffic Classification Tasks

The paper presents an approach that allows increasing the training sample and reducing class imbalance for traffic classification problems. The basic principles and architecture of generative adversarial networks are considered. The mathematical model of network traffic classification is described. The training sample taken to solve the problem has been analyzed. The data proprocessing is carried out and justified. An architecture of the generative-adversarial network is constructed and an algorithm for generating new features is developed. Machine learning models for traffic classification problem were considered and built: Logistic regression, k Nearest Neighbors, Decision tree, Random forest. A comparative analysis of the results of machine learning models without and with the generation of new features is conducted. The obtained results can be applied both in the tasks of network traffic classification, and in general cases of multiclass classification and exclusion of unbalanced features.

Prospects for the Application of Reinforcement Learning to Network Traffic Classification Tasks

The basic principles and methods of reinforcement learning are reviewed. The problems and approaches for applying a model based on reinforcement learning in the framework of attack prevention are described. The model is built and the hyperparameters of machine learning for the task of classifying network traffic are selected, and its performance on the test data set is evaluated by such quality metrics as accuracy and completeness. The dataset used to implement an agent for selecting the optimal defense strategy for a particular attack has been finalized. Developed an algorithm for using a reinforcement learning neural network for the traffic classification task. A table of rules and rewards for the problem is generated. An agent has been developed and trained to interact with the system. We describe the application of reinforcement learning to the traffic classification task.