Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Recently, there has been a lot of success in the development of effective binary classifiers. Although many statistical classification techniques have natural multiclass extensions, some, such as the support vector machines, do not. The existing techniques for mapping multiclass problems onto a set of simpler binary classification problems run into serious efficiency problems when there are hundreds or even thousands of classes, and these are the scenarios where this paper's contributions shine. We introduce the concept of correlation and joint probability of base binary learners. We learn these properties during the training stage, group the binary leaner's based on their independence and, with a Bayesian approach, combine the results to predict the class of a new instance. Finally, we also discuss two additional strategies: one to reduce the number of required base learners in the multiclass classification, and another to find new base learners that might best complement the existing set. We use these two new procedures iteratively to complement the initial solution and improve the overall performance. This paper has two goals: finding the most discriminative binary classifiers to solve a multiclass problem and keeping up the efficiency, i.e., small number of base learners. We validate and compare the method with a diverse set of methods of the literature in several public available datasets that range from small (10 to 26 classes) to large multiclass problems (1000 classes) always using simple reproducible scenarios.

Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches

Abacus: Accurate behavioral classification of P2P-TV traffic

In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse- and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.

Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM

This paper proposes a novel approach that combines specialized pairwise classifiers trained with different feature subsets for facial expression classification. The proposed approach first detects and extracts automatically faces from images. Next, the face is split into several regular zones and textural features are extracted from each zone to capture local information. The features extracted from all zones are concatenated to model the whole face. A pairwise approach that considers all pairs of classes and a hybrid feature selection strategy is used to both reduce the dimensionality and to select relevant features to discriminate between specific pairs of classes. Several pairwise classifiers are then trained with such pairwise feature subsets. At the end, given a new face image, all features are extracted from such a face, but only the previously selected subset of features is inputted to each pairwise classifier. The output of all pairwise classifiers is combined using a majority voting rule to decide on the facial expression. Experiments have been carried out on three publicly available datasets (JAFFE, CK and TFEID) and the correct classification rates of 99.05%, 98.07% and 99.63% were achieved respectively. Therefore, the pairwise approach is effective to discriminate between different facial expressions and the results achieved by the proposed approach are slightly better than several current approaches.

Facial expression recognition using a pairwise feature selection and classification approach

Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic has become one of the most significant portion of the network traffic. Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. App-level classification of P2P traffic, especially without payload feature detection, is still a challenging problem. This paper proposes a new method for P2P traffic identification and app-level classification, which merely uses transport layer information. The method uses optimized Support Vector Machines to perform large learning tasks, which is common in network traffic identification. The experimental results show that the proposed method has high efficiency and promising accuracy.

Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines

The support vector machine (SVM) has an excellent ability to solve binary classification problems. How to process multi-class problems with SVM is one of the present focuses. Among the existing multi-class SVM methods include one-against-one method, one-against-all method and some others. This paper presents an improved technique of one-against-one method that can largely reduce the number of the hyper-planes and speed up the predicting process. The experimental results show that the proposed method not only has promising accuracy and less training time, but also significantly improves the predicting speed in comparison with traditional one-against-one and one-against-all method.

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An Improvement of One-Against-One Method for Multi-Class Support Vector Machine

Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic has become one of the most significant portions of the network traffic. Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. Application level classification of P2P traffic, especially without payload feature detection, is still a challenging problem. This paper proposes a new method for P2P traffic identification and application level classification, which merely uses transport layer information. The method uses support vector machines which have been optimized for performing large learning tasks, rendering that this method become more suitable for large network traffic. The experimental results show that this method achieved high efficiency and is suitable for real-time identification. And carefully tuning the parameters could make the method achieve high accuracy.

Solving P2P Traffic Identification Problems Via Optimized Support Vector Machines

Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic, being a significant portion of the network traffic today, has constituted a highly desirable class for identification. How to improve the accuracy of the P2P traffic identification efficiently is still a challenging problem. The support vector machine (SVM) is a powerful learning mechanism and has shown remarkable success in many applications. In this paper, we propose a new approach for P2P traffic identification, which uses the support vector machine and a new technology called smooth processing. The experiments of identifying P2P traffic show that the generalization performance and the accuracy of identification are improved significantly compared to that of the traditional methods

Applying Support Vector Machine to P2P Traffic Identification with Smooth Processing

https://doi.org/10.1016/j.psj.2022.101892

Yang Liu

Papers

Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines

An Improvement of One-Against-One Method for Multi-Class Support Vector Machine

Solving P2P Traffic Identification Problems Via Optimized Support Vector Machines

Applying Support Vector Machine to P2P Traffic Identification with Smooth Processing

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