Delegation constraint management

The paper addresses the issue of providing access control via delegation and constraint management across multiple security domains. Specifically, this paper proposes a novel Delegation Constraint Management model to manage and enforce delegation constraints across security domains. An algorithm to trace the authority of delegation constraints is introduced as well as an algorithm to form a delegation constraint set and detect/prevent potential conflicts. The algorithms and the management model are built upon a set of formal definitions of delegation constraints. In addition, a constraint profile based on XACML is proposed as a means to express the delegation constraint. The paper also includes a protocol to exchange delegation constraints (in the form of user commitments) between the involved entities in the delegation process.

On a taxonomy of delegation

Delegation, from the technical point of view, is widely considered as a potential approach in addressing the problem of providing dynamic access control decisions in activities with a high level of collaboration, either within a single security domain or across multiple security domains. Although delegation continues to attract significant attention from the research community, presently, there is no published work that presents a taxonomy of delegation concepts and models. This paper intends to address this gap by presenting a set of taxonomic criteria relevant to the concept of delegation and applies the taxonomy to a selection of significant delegation models published in the literature.

A model for constraint and delegation management

This paper introduces a model to facilitate delegation, including ad-hoc delegation, in cross security domain activities. Specifically, this paper proposes a novel delegation constraint management model to manage and track delegation constraints across security domains. An algorithm to trace the authority of delegation constraints is introduced as well as an algorithm to form a delegation constraint set and detect/prevent potential conflicts. The algorithms and the management model are built upon a set of formal definitions of delegation constraints.

On a taxonomy of delegation

Delegation, from a technical point of view, is widely considered as a potential approach in addressing the problem of providing dynamic access control decisions in activities with a high level of collaboration, either within a single security domain or across multiple security domains. Although delegation continues to attract significant attention from the research community, presently, there is no published work that presents a taxonomy of delegation concepts and models. This article intends to address this gap by presenting a set of taxonomic criteria relevant to the concept of delegation. This article also applies the taxonomy to a selection of significant delegation models published in the literature.

Commitment issues in delegation process

Delegation is a powerful mechanism to provide flexible and dynamic access control decisions. Delegation is particularly useful in federated environments where multiple systems, with their own security autonomy, are connected under one common federation. Although many delegation schemes have been studied, current models do not seriously take into account the issue of delegation commitment of the involved parties. In order to address this issue, this paper introduces a new mechanism to help parties involved in the delegation process to express commitment constraints, perform the commitments and track the committed actions. This mechanism looks at two different aspects: pre-delegation commitment and post-delegation commitment. In pre-delegation commitment, this mechanism enables the involved parties to express the delegation constraints and address those constraints. The post-delegation commitment phase enables those parties to inform the delegator and service providers how the commitments are conducted. This mechanism utilises a modified SAML assertion structure to support the proposed delegation and constraint approach.

Multi-version documents : A digitisation solution for textual cultural artefacts

Textual cultural heritage artefacts present two serious problems for the encoder: how to record different or revised versions of the same work, and how to encode conflicting perspectives of the text using markup. Both are forms of textual variation, and can be accurately recorded using a multi-version document, based on a minimally redundant directed graph that cleanly separates variation from content.

Recognising audio-visual speech in vehicles using the AVICAR database

Interacting with technology within a vehicle environment using a voice interface can greatly reduce the effects of driver distraction. Most current approaches to this problem only utilise the audio signal, making them susceptible to acoustic noise. An obvious approach to circumvent this is to use the visual modality in addition. However, capturing, storing and distributing audio-visual data in a vehicle environment is very costly and difficult. One current dataset available for such research is the AVICAR [1] database. Unfortunately this database is largely unusable due to timing mismatch between the two streams and in addition, no protocol is available. We have overcome this problem by re-synchronising the streams on the phone-number portion of the dataset and established a protocol for further research. This paper presents the first audio-visual results on this dataset for speaker-independent speech recognition. We hope this will serve as a catalyst for future research in this area.

Stronger difficulty notions for client puzzles and denial-of-service-resistant protocols

Client puzzles are meant to act as a defense against denial of service (DoS) attacks by requiring a client to solve some moderately hard problem before being granted access to a resource. However, recent client puzzle difficulty definitions (Stebila and Ustaoglu, 2009; Chen et al., 2009) do not ensure that solving n puzzles is n times harder than solving one puzzle. Motivated by examples of puzzles where this is the case, we present stronger definitions of difficulty for client puzzles that are meaningful in the context of adversaries with more computational power than required to solve a single puzzle. A protocol using strong client puzzles may still not be secure against DoS attacks if the puzzles are not used in a secure manner. We describe a security model for analyzing the DoS resistance of any protocol in the context of client puzzles and give a generic technique for combining any protocol with a strong client puzzle to obtain a DoS-resistant protocol.

The delta-phase spectrum with application to voice activity detection and speaker recognition

For several reasons, the Fourier phase domain is less favored than the magnitude domain in signal processing and modeling of speech. To correctly analyze the phase, several factors must be considered and compensated, including the effect of the step size, windowing function and other processing parameters. Building on a review of these factors, this paper investigates a spectral representation based on the Instantaneous Frequency Deviation, but in which the step size between processing frames is used in calculating phase changes, rather than the traditional single sample interval. Reflecting these longer intervals, the term delta-phase spectrum is used to distinguish this from instantaneous derivatives. Experiments show that mel-frequency cepstral coefficients features derived from the delta-phase spectrum (termed Mel-Frequency delta-phase features) can produce broadly similar performance to equivalent magnitude domain features for both voice activity detection and speaker recognition tasks. Further, it is shown that the fusion of the magnitude and phase representations yields performance benefits over either in isolation.

A distributed protocol for object tracking in wireless multimedia sensor networks

We consider the problem of object tracking in a wireless multimedia sensor network (we mainly focus on the camera component in this work). The vast majority of current object tracking techniques, either centralised or distributed, assume unlimited energy, meaning these techniques don't translate well when applied within the constraints of low-power distributed systems. In this paper we develop and analyse a highly-scalable, distributed strategy to object tracking in wireless camera networks with limited resources. In the proposed system, cameras transmit descriptions of objects to a subset of neighbours, determined using a predictive forwarding strategy. The received descriptions are then matched at the next camera on the objects path using a probability maximisation process with locally generated descriptions. We show, via simulation, that our predictive forwarding and probabilistic matching strategy can significantly reduce the number of object-misses, ID-switches and ID-losses; it can also reduce the number of required transmissions over a simple broadcast scenario by up to 67%. We show that our system performs well under realistic assumptions about matching objects appearance using colour.

Noise robust voice activity detection using normal probability testing and time-domain histogram analysis

This paper presents a method of voice activity detection (VAD) suitable for high noise scenarios, based on the fusion of two complementary systems. The first system uses a proposed non-Gaussianity score (NGS) feature based on normal probability testing. The second system employs a histogram distance score (HDS) feature that detects changes in the signal through conducting a template-based similarity measure between adjacent frames. The decision outputs by the two systems are then merged using an open-by-reconstruction fusion stage. Accuracy of the proposed method was compared to several baseline VAD methods on a database created using real recordings of a variety of high-noise environments.

Person-independent facial expression detection using constrained local models

In automatic facial expression detection, very accurate registration is desired which can be achieved via a deformable model approach where a dense mesh of 60-70 points on the face is used, such as an active appearance model (AAM). However, for applications where manually labeling frames is prohibitive, AAMs do not work well as they do not generalize well to unseen subjects. As such, a more coarse approach is taken for person-independent facial expression detection, where just a couple of key features (such as face and eyes) are tracked using a Viola-Jones type approach. The tracked image is normally post-processed to encode for shift and illumination invariance using a linear bank of filters. Recently, it was shown that this preprocessing step is of no benefit when close to ideal registration has been obtained. In this paper, we present a system based on the Constrained Local Model (CLM) which is a generic or person-independent face alignment algorithm which gains high accuracy. We show these results against the LBP feature extraction on the CK+ and GEMEP datasets.

Detecting collusive fraud in enterprise resource planning systems

In today's technological age, fraud has become more complicated, and increasingly more difficult to detect, especially when it is collusive in nature. Different fraud surveys showed that the median loss from collusive fraud is much greater than fraud perpetrated by a single person. Despite its prevalence and potentially devastating effects, collusion is commonly overlooked as an organizational risk. Internal auditors often fail to proactively consider collusion in their fraud assessment and detection efforts. In this paper, we consider fraud scenarios with collusion. We present six potentially collusive fraudulent behaviors and show their detection process in an ERP system. We have enhanced our fraud detection framework to utilize aggregation of different sources of logs in order to detect communication and have further enhanced it to render it system-agnostic thus achieving portability and making it generally applicable to all ERP systems.

Noise robust voice activity detection using features extracted from the time-domain autocorrelation function

This paper presents a method of voice activity detection (VAD) for high noise scenarios, using a noise robust voiced speech detection feature. The developed method is based on the fusion of two systems. The first system utilises the maximum peak of the normalised time-domain autocorrelation function (MaxPeak). The second zone system uses a novel combination of cross-correlation and zero-crossing rate of the normalised autocorrelation to approximate a measure of signal pitch and periodicity (CrossCorr) that is hypothesised to be noise robust. The score outputs by the two systems are then merged using weighted sum fusion to create the proposed autocorrelation zero-crossing rate (AZR) VAD. Accuracy of AZR was compared to state of the art and standardised VAD methods and was shown to outperform the best performing system with an average relative improvement of 24.8% in half-total error rate (HTER) on the QUT-NOISE-TIMIT database created using real recordings from high-noise environments.

Bayes Factor based speaker segmentation for speaker diarization

This paper proposes the use of the Bayes Factor as a distance metric for speaker segmentation within a speaker diarization system. The proposed approach uses a pair of constant sized, sliding windows to compute the value of the Bayes Factor between the adjacent windows over the entire audio. Results obtained on the 2002 Rich Transcription Evaluation dataset show an improved segmentation performance compared to previous approaches reported in literature using the Generalized Likelihood Ratio. When applied in a speaker diarization system, this approach results in a 5.1% relative improvement in the overall Diarization Error Rate compared to the baseline.