38,902 research outputs found
Incremental High Throughput Network Traffic Classifier
Today’s network traffic are dynamic and fast. Con-ventional network traffic classification based on flow feature and data mining are not able to process traffic efficiently. Hardware based network traffic classifier is needed to be adaptable to dynamic network state and to provide accurate and updated classification at high speed. In this paper, a hardware architecture of online incremental semi-supervised algorithm is proposed. The hardware architecture is designed such that it is suitable to be incorporated in NetFPGA reference switch design. The experimental results on real datasets show that with only 10% of labeled data, the proposed architecture can perform online classification of network traffic at 1Gbps bitrate with 91% average accuracy without loosing any flows
Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols
© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy
MULTI-DIMENSIONAL PROFILING OF CYBER THREATS FOR LARGE-SCALE NETWORKS
Current multi-domain command and control computer networks require significant oversight to ensure acceptable levels of security. Firewalls are the proactive security management tool at the network’s edge to determine malicious and benign traffic classes. This work aims to develop machine learning algorithms through deep learning and semi-supervised clustering, to enable the profiling of potential threats through network traffic analysis within large-scale networks. This research accomplishes these objectives by analyzing enterprise network data at the packet level using deep learning to classify traffic patterns. In addition, this work examines the efficacy of several machine learning model types and multiple imbalanced data handling techniques. This work also incorporates packet streams for identifying and classifying user behaviors. Tests of the packet classification models demonstrated that deep learning is sensitive to malicious traffic but underperforms in identifying allowed traffic compared to traditional algorithms. However, imbalanced data handling techniques provide performance benefits to some deep learning models. Conversely, semi-supervised clustering accurately identified and classified multiple user behaviors. These models provide an automated tool to learn and predict future traffic patterns. Applying these techniques within large-scale networks detect abnormalities faster and gives network operators greater awareness of user traffic.Outstanding ThesisCaptain, United States Marine CorpsApproved for public release. Distribution is unlimited
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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