The frequent and large-scale network attacks have led to an increased need for
developing techniques for analyzing network traffic. If efficient analysis tools were
available, it could become possible to detect the attacks, anomalies and to appropriately
take action to contain the attacks before they have had time to propagate across the
network.
In this dissertation, we suggest a technique for traffic anomaly detection based on
analyzing the correlation of destination IP addresses and distribution of image-based
signal in postmortem and real-time, by passively monitoring packet headers of traffic.
This address correlation data are transformed using discrete wavelet transform for
effective detection of anomalies through statistical analysis. Results from trace-driven
evaluation suggest that the proposed approach could provide an effective means of
detecting anomalies close to the source. We present a multidimensional indicator using
the correlation of port numbers as a means of detecting anomalies.
We also present a network measurement approach that can simultaneously detect,
identify and visualize attacks and anomalous traffic in real-time. We propose to
represent samples of network packet header data as frames or images. With such a
formulation, a series of samples can be seen as a sequence of frames or video. Thisenables techniques from image processing and video compression such as DCT to be
applied to the packet header data to reveal interesting properties of traffic. We show that
??scene change analysis?? can reveal sudden changes in traffic behavior or anomalies. We
show that ??motion prediction?? techniques can be employed to understand the patterns of
some of the attacks. We show that it may be feasible to represent multiple pieces of data
as different colors of an image enabling a uniform treatment of multidimensional packet
header data.
Measurement-based techniques for analyzing network traffic treat traffic volume
and traffic header data as signals or images in order to make the analysis feasible. In this
dissertation, we propose an approach based on the classical Neyman-Pearson Test
employed in signal detection theory to evaluate these different strategies. We use both of
analytical models and trace-driven experiments for comparing the performance of
different strategies. Our evaluations on real traces reveal differences in the effectiveness
of different traffic header data as potential signals for traffic analysis in terms of their
detection rates and false alarm rates. Our results show that address distributions and
number of flows are better signals than traffic volume for anomaly detection. Our results
also show that sometimes statistical techniques can be more effective than the NP-test
when the attack patterns change over time.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2312 |
| Date | 29 August 2005 |
| Creators | Kim, Seong Soo |
| Contributors | Reddy, A. L. Narasimha |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 2413150 bytes, electronic, application/pdf, born digital |
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