Demands for efficiency in computer networks have led to the development of a set of standard Internet cryptographic protocols that leak meaningful information about the confidential data they transmit. Information leakage occurs because, although the current cryptographic protocols employ strong ciphers to protect the contents of their messages, they do not attempt to hide patterns in when messages are sent, or how large these messages are. Differences in network-layer behavior, which occur as a result of differences in application protocols or user action, thus remain observable even after encryption.
This thesis explores ways in which these differences can be exploited, both by well-meaning network administrators and by malicious eavesdroppers, to infer sensitive information from encrypted traffic. Using a variety of machine learning techniques, we show how patterns in packet sizes and timing can be used to learn about encrypted traffic with a surprising degree of accuracy. First, we show how application protocols can be recognized in traffic encrypted with an end-to-end cryptographic protocl like
|School:||The Johns Hopkins University|
|School Location:||United States -- Maryland|
|Source:||DAI-B 69/04, Dissertation Abstracts International|
|Keywords:||Cryptography, Encryption, Information leakage, Network security, Network traffic|
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