Privacy enhancement in anonymous network channels using multimodality injection

• Published in: Security and communication networks 2015-11, Vol.8 (16), p.2917-2932
• Main Authors: Nia, Mehran Alidoost, Atani, Reza Ebrahimi, Ruiz-Martínez, Antonio
• Format: Article
• Language: English
• Subjects: anonymous network channels; Channels; Computer information security; Immunity; Level (quantity); multimodality injection; Network analysis; network data analysis; Networks; Random walk; self-avoiding random walk; Tasks; weighted crypto-type
• ISSN: 1939-0114; 1939-0122
• DOI: 10.1002/sec.1219

The increase of the capacity of processing units and the growth of distributed computing make easy to collect and process information of Internet traffic flows. This information, however, can be used to perform attacks in anonymous communications that could compromise privacy. With the aim of preventing these attacks, we propose a scheme that implements a multimodal behavior using the random walk theory and crypto‐types. The random walk mechanism is responsible for generating network patterns, and the cryptotype performs the micro‐encryption tasks using series of quantum‐resistant cryptography methods through the anonymous channel. The result shows that using this technique, we can prevent network analysis attacks by means of the generation of a different pattern in each execution for the same set of data. Namely, the experiments we have developed indicate that the average rate of true detections of application behaviors made by intruders does not exceed 24%. Thus, this multimodal pattern gives a high level of immunity against data analysis attacks because the intruders could consider the generated patterns as the typical patterns. Copyright © 2015 John Wiley & Sons, Ltd. In order to prevent network analysis attacks, we propose a scheme that implements a multimodal behavior using the random walk theory and crypto‐types. The random walk is responsible for generating network patterns, and the crypto‐type performs the micro‐encryption tasks through the anonymous channel. The experiments we have developed indicate that the average rate of true detections of application behaviors made by intruders does not exceed 24%. Thus, this multimodal pattern gives a high level of immunity against network analysis attacks.