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Data Mining and Deep Learning Systems for Network Traffic Classification and Characterization at Scale.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Data Mining and Deep Learning Systems for Network Traffic Classification and Characterization at Scale./
Author:	Kapoor, Maya.
Description:	1 online resource (137 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379919177

Data Mining and Deep Learning Systems for Network Traffic Classification and Characterization at Scale.
Kapoor, Maya.

Data Mining and Deep Learning Systems for Network Traffic Classification and Characterization at Scale. - 1 online resource (137 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2023.

Includes bibliographical references

The real, complex network environment consists of an ever-increasingly diverse and large amount of data encapsulated in packets. Surveillance and monitoring of this traffic is a necessary task for law enforcement, cybersecurity, and intelligence agencies. Intercepted network traffic must be classified into multiple categories, such as the protocol encapsulation layers contained, application it originates from, user generating the traffic, and the traffic's malicious or benign nature. There is a lack of solutions which are able to classify packets individually without flow-based features. In order to address the gaps in current traffic classification and DPI techniques, we propose the initial release of the Forager toolkit, a software consisting of tools to extract hidden representations from individual packets and use these features in deep learning models to perform traffic classification. It uses data mining techniques to perform automatic generation of regular expression signatures, locality-sensitive hash fingerprints, and matrix and point cloud representations of packets. These are used as input features for corresponding deep learning models which can perform traffic classification on single packets in a real system. The models are multi-modal to capture multiple angles and dimensions of features for increased complexity of classification problems. They can be run in parallel for optimal throughput and scalability. Our experiments use these models in multiple configurations and scenarios to demonstrate superior performance and classification capability to advance the state of the art in complex network traffic surveillance and hidden representation learning.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379919177Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

CybersecurityIndex Terms--Genre/Form:

554714
Electronic books.

Data Mining and Deep Learning Systems for Network Traffic Classification and Characterization at Scale.
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520 $a The real, complex network environment consists of an ever-increasingly diverse and large amount of data encapsulated in packets. Surveillance and monitoring of this traffic is a necessary task for law enforcement, cybersecurity, and intelligence agencies. Intercepted network traffic must be classified into multiple categories, such as the protocol encapsulation layers contained, application it originates from, user generating the traffic, and the traffic's malicious or benign nature. There is a lack of solutions which are able to classify packets individually without flow-based features. In order to address the gaps in current traffic classification and DPI techniques, we propose the initial release of the Forager toolkit, a software consisting of tools to extract hidden representations from individual packets and use these features in deep learning models to perform traffic classification. It uses data mining techniques to perform automatic generation of regular expression signatures, locality-sensitive hash fingerprints, and matrix and point cloud representations of packets. These are used as input features for corresponding deep learning models which can perform traffic classification on single packets in a real system. The models are multi-modal to capture multiple angles and dimensions of features for increased complexity of classification problems. They can be run in parallel for optimal throughput and scalability. Our experiments use these models in multiple configurations and scenarios to demonstrate superior performance and classification capability to advance the state of the art in complex network traffic surveillance and hidden representation learning.
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