Privacy enhancement in anonymous network channels using multimodality injection

Abstract

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.

On detecting unidentified network traffic using pattern-based random walk

“…Results of earlier work on random based anonymous channels led to designing random walk based pattern simulator called RAPS . The RAPS can generate random patterns for anonymous channels using random walk.…”

Detecting new generations of threats using attribute‐based attack graphs

“…Traffic analysis is useful for security enhancement in an enterprise network; however, it would endanger privacy and security of users' applications [4]. Some methods are able to detect encrypted network flows using traffic patterns.…”

“…Depending on the application, however, it is possible to perform the entire analysis without some of them [3]. In applications such as Tor, which uses multi-layered encrypted packets, direction is not accessible [4]. To perform an accurate analysis, we need to access at least two of the mentioned features.…”

“…In such networks, in order to update distance metrics dynamically, random walks are deployed. Another important application of random walk is traffic-engineering [4]. Not only it is used in order to detect cyber-threats, but it is also applied to network-based applications to improve the privacy of users through the Internet [14].…”

“…In this situation, our threat database would be clairvoyant, and matching each newly sampled traffic to a record from the database would be simple. If the malicious traffic is identified, monitoring tools such as firewalls can block it, even though adversaries have certain techniques to hide malicious traffic from the external observer, which is done by making fundamental changes in network flow patterns [4].…”