The research community has begun looking for IP traffic classification techniques that do not rely on `well known? TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature. We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these requirements. Open issues and challenges in the field are also discussed.

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A survey of techniques for internet traffic classification using machine learning

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.

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Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Artificial intelligence (AI) has been applied to the telecommunications industry for more than a decade. The purpose of this paper is to examine the application of AI in the telecommunications industry sector. Our research finds that AI's first main application in telecommunications is in the network management area. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques which are most promising for the future. The research also finds that different AI techniques have their unique applications in the telecommunications industry.

Artificial intelligence applications in the telecommunications industry

This paper describes our initial implementation of a domain-independent Integrated Learning System (ILS), and one application, which, through its own experience, discovers how to control a telecommunications network. ILS provides a framework for integrating several heterogeneous learning agents, in this case implementations of inductive, search-based and knowledge-based learning. These agents, written in various languages and executing on various platforms, cooperate to improve problem-solving performance. ILS also includes a central controller, called The Learning Coordinator (TLC), which manages control flow and communication between the agents using a high-level communication protocol. The agents provide TLC with expert advice. TLC chooses which suggestion to adopt and performs the appropriate actions. At intervals, the agents can inspect the results of the TLC'S actions and use this feedback to learn, improving the value of their future advice. At present ILS is being extensively tested, and the initial results are promising.

ILS: a framework for multi-paradigmatic learning

Recently, damage caused by DDoS attacks increases year by year. Along with the advancement of communication technology, this kind of attack also evolves and it has become more complicated and hard to detect using flash crowd agent, slow rate attack and also amplification attack that exploits a vulnerability in DNS server. Fast detection of the DDoS attack, quick response mechanisms and proper mitigation are a must for an organization. An investigation has been performed on DDoS attack and it analyzes the details of its phase using machine learning technique to classify the network status. In this paper, we propose a hybrid KNN-SVM method on classifying, detecting and predicting the DDoS attack. The simulation result showed that each phase of the attack scenario is partitioned well and we can detect precursors of DDoS attack as well as the attack itself.

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An Evaluation on KNN-SVM Algorithm for Detection and Prediction of DDoS Attack

One of the goals of Machine Learning is the production of software that can improve itself. Such software can learn from experience and adapt to changing situations and requirements. In addition, such software can refine its knowledge-base, perhaps leading to a level of expertise beyond that of human experts.This paper describes NETMAN, a knowledge-based program that uses a machine learning technique, Knowledge-based Learning, in the domain of Network Traffic Control. NETMAN's task is to maximize call completion in a circuit-switched telecommunications network. NETMAN learns from its own experiences and by observing the actions of other agents.NETMAN is one of the components of ILS (Integrated Learning System), which contains implementations of several learning paradigms working together to improve problem-solving performance. NETMAN combines two machine learning paradigms: Explanation-Based Learning and Empirical Learning.

Bernard Silver

Papers

Netman: a learning network traffic controller