Unwanted Traffic Detection and Control Based on Trust Management

Zheng Yan*, Raimo Kantola, Lifang Zhang, Yutan Ma

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

1 Citation (Scopus)

Abstract

Networks such as the Internet, mobile cellular networks, and self-organized ad hoc networks have dramatically changed our daily life and brought tremendous benefits to us. However, they are also bogged down by unwanted traffic, which is malicious, harmful, or unexpected for its receivers. In order to control the unwanted traffic over the networks, especially the mobile Internet, we propose a generic scheme named TruCon for unwanted traffic detection and control based on trust management in this chapter. It can control unwanted traffic from its source to destinations in a personalized manner according to trust evaluation at a global trust operator, traffic and behavior analysis at hosts, and traffic observation at network service providers. Thus, the proposed scheme can conduct unwanted traffic detection and control by integrating distributed and centralized functions and supporting both defensive and offensive approaches of unwanted traffic control. We successfully applied the scheme to control SMS spam and unwanted contents in pervasive social networking and implemented it under the infrastructure of software-defined networking (SDN). System implementation and evaluation showed that the scheme is effective with regard to accuracy and efficiency for intrusion detection and unwanted traffic control. It is also robust against a number of internal misleading system attacks, such as hide evidence attack, bad-mouthing attack, and on-off attack, playing in conjunction with traffic intrusions. Meanwhile, the scheme can provide personalized unwanted traffic control based on unwanted traffic detection behaviors.

Original languageEnglish
Title of host publicationInformation Fusion for Cyber-Security Analytics
EditorsIzzat M. Alsmadi, George Karabatis, Ahmed AlEroud
Pages77-109
Number of pages33
ISBN (Electronic)978-3-319-44257-0
DOIs
Publication statusPublished - 1 Jan 2017
MoE publication typeA3 Part of a book or another research book

Publication series

NameStudies in computational intelligence
PublisherSpringer
Volume691
ISSN (Print)1860-949X

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