Radio frequency traffic classification over WLAN

Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types

Robustness Comparison of Moving Convolutive Source Separation Techniques

A multitude of convolutive blind source separation algorithms exist, a small number of which can deal with moving sources. The main assumption for moving source algorithms is that for a small amount of time the sources are approximately stationary and hence the mixing conditions are slowly varying. In reality, speech sources are likely to fall silent and hence the mixing conditions will jump to new values. This paper compares a number of blind source separation algorithms focusing on robustness to source and jammer movement. Acoustic models of a single reflector, a studio and a meeting room are used to generate the source mixtures. In addition, weight robustness is assessed using real world recordings from a studio

Comparison of Subjective and Objective Evaluation Methods for Audio Source Separation

The evaluation of audio separation algorithms can either be performed objectively by calculation of numerical measures, or subjectively through listening tests. Although objective evaluation is inherently more straightforward, subjective listening tests are still essential in determining the perceived quality of separation. This paper aims to find relationships between objective and subjective results so that numerical values can be translated into perceptual criteria. A generic audio source separation system was modelled which provided varying levels of interference, noise and artifacts. This enabled a full spread of objective measurement values to be obtained. Extensive tests were performed utilising the output synthesised by this separation model. The relationships found were presented and the factors of prime importance were determined

Radio Frequency Traffic Classification Over WLAN

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