Sascha Hauke

Trust as a facilitator in cloud computing: a survey

AbstractCloud computing offers massively scalable, elastic resources (e.g., data, computing power, and services) over the internet from remote data centres to the consumers. The growing market penetration, with an evermore diverse provider and service landscape, turns Cloud computing marketplaces a highly competitive one. In this highly competitive and distributed service environment, the assurances are insufficient for the consumers to identify the dependable and trustworthy Cloud providers.This paper provides a landscape and discusses incentives and hindrances to adopt Cloud computing from Cloud consumers’ perspective. Due to these hindrances, potential consumers are not sure whether they can trust the Cloud providers in offering dependable services. Trust-aided unified evaluation framework by leveraging trust and reputation systems can be used to assess trustworthiness (or dependability) of Cloud providers. Hence, cloud-related specific parameters (QoS + ) are required for the trust and reputation systems in Cloud environments. We identify the essential properties and corresponding research challenges to integrate the QoS + parameters into trust and reputation systems. Finally, we survey and analyse the existing trust and reputation systems in various application domains, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) why trust establishment is important in the Cloud computing landscape, 2) how trust can act as a facilitator in this context and 3) what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

BackgroundMaturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.ResultsWe tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.ConclusionsOur analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.

Fusion of Opinions under Uncertainty and Conflict -- Application to Trust Assessment for Cloud Marketplaces

The fusion of trust relevant information provided by multiple sources is one of the major challenges of trust establishment, which in turn is a key research topic in the growing field of cloud computing. We present a novel fusion operator for combining information from different sources, representing propositions under uncertainty. The operator especially extend the state-of-the-art by explicitly considering weights and the handling of conflicting dependent opinions. We provide a use case that demonstrates the applicability of our approach and shows the capability of the novel operator to a more reliable and transparent assessment of the trustworthiness of cloud providers.

Insights into the classification of small GTPases

In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences.

Communicating and visualising multicriterial trustworthiness under uncertainty

Visualisations are often used to communicate trust-worthiness to end users. Showing a number of stars, for example, is a well-known practice in e-commerce applications to communicate the quality of a product or service. Many products or services also have their quality - in terms of trustworthiness - described along more than one dimension, so that not only an overall trust score has to be communicated, but multiple scores, one for each dimension. Current visualisations of such a multicriterial trustworthiness are often based on the display of multiple individual star-like interfaces - a practice that offers room for improvement with regard to intuitive understanding of the displayed trust information. In this paper, we present T-Viz, a trust visualisation based on radar plots and pie charts. T-Viz concurrently shows multiple trust scores, one for each dimension, along with an aggregated trust score. Moreover, T-Viz also shows a reliability measure for every trust score graphically, in the form of a certainty score. The evaluation results from a pilot study with eleven participants indicate that T-Viz is an intuitive, comprehensible and clear interface. It succeeds at visualising and communicating multicriterial trust scores under uncertainty in one, easy to understand, graphical representation.

An evolutionary approach for solving the rubik's cube incorporating exact methods

Solutions calculated by Evolutionary Algorithms have come to surpass exact methods for solving various problems. The Rubik’s Cube multiobjective optimization problem is one such area. In this work we present an evolutionary approach to solve the Rubik’s Cube with a low number of moves by building upon the classic Thistlethwaite’s approach. We provide a group theoretic analysis of the subproblem complexity induced by Thistlethwaite’s group transitions and design an Evolutionary Algorithm from the ground up including detailed derivation of our custom fitness functions. The implementation resulting from these observations is thoroughly tested for integrity and random scrambles, revealing performance that is competitive with exact methods without the need for pre-calculated lookup-tables.

Computational trust methods for security quantification in the cloud ecosystem

In this chapter, we provide an in-depth insight into computational trust methods that are able to reliably quantify the security level of service providers and transparently communicate that level to the users. The methods particularly consider business as well as end user requirements along with a complex specification of security assurances during security quantification. Novel trust methods are validated using formal proofs, industry-accepted security assurance datasets, and user studies.

On the Application of Supervised Machine Learning to Trustworthiness Assessment

State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world dataset, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.

Analyzing flow-based anomaly intrusion detection using Replicator Neural Networks

Defending key network infrastructure, such as Internet backbone links or the communication channels of critical infrastructure, is paramount, yet challenging. The inherently complex nature and quantity of network data impedes detecting attacks in real world settings. In this paper, we utilize features of network flows, characterized by their entropy, together with an extended version of the original Replicator Neural Network (RNN) and deep learning techniques to learn models of normality. This combination allows us to apply anomaly-based intrusion detection on arbitrarily large amounts of data and, consequently, large networks. Our approach is unsupervised and requires no labeled data. It also accurately detects network-wide anomalies without presuming that the training data is completely free of attacks. The evaluation of our intrusion detection method, on top of real network data, indicates that it can accurately detect resource exhaustion attacks and network profiling techniques of varying intensities. The developed method is efficient because a normality model can be learned by training an RNN within a few seconds only.

Towards engineering trust systems: Template-based, component-oriented assembly

Trust and reputation systems (TRS) require a composition of individual methods for prediction, selection and feedback/data generation in order to fully realise their potential. Building on recent work identifying common aspects of TRS, this paper presents a prototype of a software deployment tool for assembling TRS from individual modules. This tool is considered as a precursory support step to the systematic testing and comparison of the various facets that constitute the trust assessment process. This will support future research and provide an engineering perspective to the development of new TRS, by providing easy deployment of TRS for testing.