Fan Jieyan: Network Centric Traffic Analysis Network Centric Anomaly Detection and Network Centric Traffic Classification
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
26.11.2008
Verlag
VDMSeitenzahl
96
Maße (L/B/H)
22/15/0,6 cm
Gewicht
145 g
Sprache
Deutsch
ISBN
978-3-8364-9296-6
To provide more reliable and secure Internet
service, Internet service providers have more and
more interests in network centric traffic analysis.
This book considers this issue from two
perspectives, which are of ISP's most interest: 1)
network centric anomaly detection and 2) network
centric traffic classification.
In our study on network centric anomaly detection,
we designed an edge router based framework to detect
anomaly in the first place they enter network; we
proposed the so-called two-way matching features,
which are effective indicators of network anomalies;
and we creatively considered spatial and temporal
correlation among edge routers at the same time.
To tap the potential profits made by multimedia
services, ISPs are of much interest to detect voice
and video traffic. Yet, to our best knowledge no
existing approaches are available to separate
between voice and video. To solve the problem, we
creatively applied spectral analysis techniques to
extract regularities in multimedia traffic and used
minimum distance to subspace as classification
metric. Results demonstrate the effectiveness and
robustness of our approach.
service, Internet service providers have more and
more interests in network centric traffic analysis.
This book considers this issue from two
perspectives, which are of ISP's most interest: 1)
network centric anomaly detection and 2) network
centric traffic classification.
In our study on network centric anomaly detection,
we designed an edge router based framework to detect
anomaly in the first place they enter network; we
proposed the so-called two-way matching features,
which are effective indicators of network anomalies;
and we creatively considered spatial and temporal
correlation among edge routers at the same time.
To tap the potential profits made by multimedia
services, ISPs are of much interest to detect voice
and video traffic. Yet, to our best knowledge no
existing approaches are available to separate
between voice and video. To solve the problem, we
creatively applied spectral analysis techniques to
extract regularities in multimedia traffic and used
minimum distance to subspace as classification
metric. Results demonstrate the effectiveness and
robustness of our approach.
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