Using decoys to block SPIT in the IMS

Includes bibliographical references (leaves 106-111)In recent years, studies have shown that 80-85% of e-mails sent were spam. Another form of spam that has just surfaced is VoIP (Voice over Internet Telephony) spam. Currently, VoIP has seen an increasing numbers of users due to the cheap rates. With the introduction of the IMS (IP Multimedia Subsystem), the number of VoIP users are expected to increase dramatically. This calls for a cause of concern, as the tools and methods that have been used for blocking email spam may not be suitable for real-time voice calls. In addition, VoIP phones will have URI type addresses, so the same methods that were used to generate automated e-mail spam messages can be employed for unsolicited voice calls. Spammers will always be present to take advantage of and adapt to trends in communication technology. Therefore, it is important that IMS have structures in place to alleviate the problems of spam. Recent solutions proposed to block SPIT (Spam over Internet Telephony) have the following shortcomings: restricting the users to trusted senders, causing delays in voice call set-up, reducing the efficiency of the system by increasing burden on proxies which have to do some form of bayesian or statistical filtering, and requiring dramatic changes in the protocols being used. The proposed decoying system for the IMS fits well with the existing protocol structure, and customers are oblivious of its operation

Band Limited Signals Observed Over Finite Spatial and Temporal Windows: An Upper Bound to Signal Degrees of Freedom

The study of degrees of freedom of signals observed within spatially diverse broadband multipath fields is an area of ongoing investigation and has a wide range of applications, including characterising broadband MIMO and cooperative networks. However, a fundamental question arises: given a size limitation on the observation region, what is the upper bound on the degrees of freedom of signals observed within a broadband multipath field over a finite time window? In order to address this question, we characterize the multipath field as a sum of a finite number of orthogonal waveforms or spatial modes. We show that (i) the "effective observation time" is independent of spatial modes and different from actual observation time, (ii) in wideband transmission regimes, the "effective bandwidth" is spatial mode dependent and varies from the given frequency bandwidth. These findings clearly indicate the strong coupling between space and time as well as space and frequency in spatially diverse wideband multipath fields. As a result, signal degrees of freedom does not agree with the well-established degrees of freedom result as a product of spatial degrees of freedom and time-frequency degrees of freedom. Instead, analogous to Shannon's communication model where signals are encoded in only one spatial mode, the available signal degrees of freedom in spatially diverse wideband multipath fields is the time-bandwidth product result extended from one spatial mode to finite modes. We also show that the degrees of freedom is affected by the acceptable signal to noise ratio (SNR) in each spatial mode.Comment: Submitted to IEEE Transactions on Signal Processin

Analysis of Degrees of Freedom of Wideband Random Multipath Fields Observed Over Time and Space Windows

In multipath systems, available degrees of freedom can be considered as a key performance indicator, since the channel capacity grows linearly with the available degrees of freedom. However, a fundamental question arises: given a size limitation on the observable region, what is the intrinsic number of degrees of freedom available in a wideband random multipath wavefield observed over a finite time interval? In this paper, we focus on answering this question by modelling the wavefield as a sum of orthogonal waveforms or spatial orders. We show that for each spatial order, (i) the observable wavefield is band limited within an effective bandwidth rather than the given bandwidth and (ii) the observation time varies from the given observation time. These findings show the strong coupling between space and time as well as space and bandwidth. In effect, for spatially diverse multipath wavefields, the classical degrees of freedom result of "time-bandwidth" product does not directly extend to "time-space-bandwidth" product.Comment: 9 pages, 2 figures, Accepted in 2014 IEEE Workshop on Statistical Signal Processin

Acoustic sensor array signal processing for biomedical applications

This thesis develops array signal processing theories for selected biomedical applications involving acoustic waves. Specifically, we consider source localization in the interior of sensor arrays for lung sound localization and efficient algorithms for photoacoustic imaging. Lung sound localization provides quantitative results to the extent and location of lung disorders. Photoacoustic imaging is important for the early detection of cancer and has numerous other biomedical applications. Previous lung sound localization methods cannot deal with multiple sources or have analytical performance measures. We propose two methods utilizing the eigen basis decomposition of the wavefield and the Minimum Variance spectrum for multiple source localization. Analytical performance measures were derived for resolution and spatial aliasing. The performance of our methods for lung sound localization together with the performance measures were proven by simulations. We consider the photoacoustic inversion problem from a frequency invariant source localization perspective. Complete series and fast photoacoustic inversion methods have not been developed for the circular and spherical sensor geometries. A new theory is developed for photoacoustic reconstruction where the source distribution is expanded with a suitable series expansion such that separating the modes in the wavefield expansion at particular frequencies, separates the information in the source expansion. This theory is applied for photoacoustic inversion using a circular acquisition geometry. The source is expanded using a Fourier Bessel series and the coefficients are estimated by processing frequencies corresponding to the Bessel zeros. The proposed method is faster than previous approaches and the derivation is valid even for finite measurement bandwidth. This new theory is flexible enough to be applied for arbitrary sensor geometries and allows the selection of a minimum number of frequency samples for reconstruction. For previous frequency domain methods, there was no way to determine the minimum number of frequency samples required. Further, numerical experiments proved the effectiveness of our approach. The extension of the proposed theory for photoacoustic inversion with a spherical array geometry was proposed. This new method expands the source distribution with a spherical Fourier Bessel series whose coefficients were now obtained by processing frequencies corresponding to the spherical Bessel zeros. Using computational order analysis and numerical experiments, this proposed method was shown to be faster than the backprojection and the Fourier series methods. To enhance the reconstruction of our proposed methods, we introduced a sub{u00AD}gradient based Total Variation (TV) minimization and an alternating projections post processing method. Both these methods were designed to handle the large data sets present in photoacoustic tomography. Applications of these two post processing ideas to previously proposed inversion methods are either difficult or impossible. The proposed inversion methods provide projection of the source distribution onto a set of basis functions. Therefore, these two post processing methods were developed to reconstruct a source distribution that preserves these projections and ensures that the source distribution is non-negative. Numerical experiments performed showed that reconstruction quality was improved by applying these two post processing methods. Further, the TV minimization method provided better reconstruction when compared to the alternating projections method