In this research, we present a investigative study on how timing-based traffic analysis attacks can be used for recovery of the speech from a Voice Over Internet Protocol (VOIP) conversation by taking advantage of the reduction or suppression of the generation of traffic whenever the sender detects a voice inactivity period. We use the simple Bayesian classifier and the complex HMM (Hidden Markov Models) classier to evaluate the performance of our attack. Then we describe the usage of acoustic features in our attack to improve the performance. We conclude by presenting a number of problems that need in-depth study in order to be effective in carrying out silence detection based attacks on VOIP systems.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/86009 |
| Date | 10 October 2008 |
| Creators | Lella, Tuneesh Kumar |
| Contributors | BETTATI, RICCARDO |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, born digital |
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