Using internet traffic self-similarity for detection of network anomalies

Dobrescu, Radu; Dobrescu, Matei; Hossu, Daniela; Taralunga, Sebastian

doi:10.1109/optim.2008.4602461

Cited by 4 publications

(2 citation statements)

References 19 publications

(12 reference statements)

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“…An example of useful data analysis in computer networks is anomaly detection in the Internet traffic . The most popular technique in this area is the self‐similarity detection by the classification of network traffic . The real‐time traffic detection causes some obstacles in the network traffic flow which is a negative parameter.…”

Section: Preliminaries and Backgroundmentioning

confidence: 99%

Privacy enhancement in anonymous network channels using multimodality injection

Nia

Atani

Ruiz‐Martínez³

2015

Security Comm Networks

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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.

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