Random oracle-based anonymous credential system for efficient attributes proof on smart devices

Attributes proof in anonymous credential systems is an effective way to balance security and privacy in user authentication; however, the linear complexity of attributes proof causes the existing anonymous credential systems far away from being practical, especially on resource-limited smart devices. For efficiency considerations, we present a novel pairing-based anonymous credential system which solves the linear complexity of attributes proof based on aggregate signature scheme. We propose two extended signature schemes, BLS+ and BGLS+, to be cryptographical building blocks for constructing anonymous credentials in the random oracle model. Identity-like information of message holder is encoded in a signature in order that the message holder can prove the possession of the input message along with the validity of a signature. We present issuance protocol for anonymous credentials embedding weak attributes which are referred to what cannot identify a user in a population. Users can prove any combination of attributes all at once by aggregating the corresponding individual credentials into one. The attributes proof protocols on AND and OR relation over multiple attributes are also given. The performance analysis shows that the aggregation-based anonymous credential system outperforms both the conventional Camenisch–Lysyanskaya pairing-based system and the accumulator-based system when prove AND and OR relation over multiple attributes, and the size of credential and public parameters are shorter as well.     

A taint based approach for automatic reverse engineering of gray-box file formats

File format vulnerabilities have been highlighted in recent years, and the performance of fuzzing tests relies heavily on the knowledge of target formats. In this paper, we present systematic algorithms and methods to automatically reverse engineer input file formats. The methodology employs dynamic taint analysis to reveal implicit relational information between input file and binary procedures, which is used for the measurement of correlations among data bytes, format segmentation and data type inference. We have implemented a prototype, and its general tests on 10 well-published binary formats yielded an average of over 85 % successful identification rate, while more detailed structural information was unveiled beyond coarse granular format analysis. Besides, a practical pseudo-fuzzing evaluation method is discussed in accordance with real-world demands on security analysis, and the evaluation results demonstrated the practical effectiveness of our system.     

Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

Evolutionary multi-objective optimization algorithms are generally employed to generate Pareto optimal solutions by exploring the search space. To enhance the performance, exploration by global search can be complemented with exploitation by combining it with local search. In this paper, we address the issues in integrating local search with global search such as: how to select individuals for local search; how deep the local search is performed; how to combine multiple objectives into single objective for local search. We introduce a Preferential Local Search mechanism to fine tune the global optimal solutions further and an adaptive weight mechanism for combining multi-objectives together. These ideas have been integrated into NSGA-II to arrive at a new memetic algorithm for solving multi-objective optimization problems. The proposed algorithm has been applied on a set of constrained and unconstrained multi-objective benchmark test suite. The performance was analyzed by computing different metrics such as Generational distance, Spread, Max spread, and HyperVolume Ratio for the test suite functions. Statistical test applied on the results obtained suggests that the proposed algorithm outperforms the state-of-art multi-objective algorithms like NSGA-II and SPEA2. To study the performance of our algorithm on a real-world application, Economic Emission Load Dispatch was also taken up for validation. The performance was studied with the help of measures such as Hypervolume and Set Coverage Metrics. Experimental results substantiate that our algorithm has the capability to solve real-world problems like Economic Emission Load Dispatch and is able to produce better solutions, when compared with NSGA-II, SPEA2, and traditional memetic algorithms with fixed local search steps.     

Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising

Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques.     

Knowledge representation using interval-valued fuzzy formal concept lattice

Formal concept analysis (FCA) is a mathematical framework for data analysis and processing tasks. Based on the lattice and order theory, FCA derives the conceptual hierarchies from the relational information systems. From the crisp setting, FCA has been extended to fuzzy environment. This extension is aimed at handling the uncertain and vague information represented in the form of a formal context whose entries are the degrees from the scale [0, 1]. The present study analyzes the fuzziness in a given many-valued context which is transformed into a fuzzy formal context, to provide an insight into generating the fuzzy formal concepts from the fuzzy formal context. Furthermore, considering that a major problem in FCA with fuzzy setting is to reduce the number of fuzzy formal concepts thereby simplifying the corresponding fuzzy concept lattice structure, the current paper solves the problem by linking an interval-valued fuzzy graph to the fuzzy concept lattice. For this purpose, we propose an algorithm for generating the interval-valued fuzzy formal concepts. To measure the weight of fuzzy formal concepts, an algorithm is proposed using Shannon entropy. The knowledge represented by formal concepts using interval-valued fuzzy graph is compared with entropy-based-weighted fuzzy concepts at chosen threshold.     

Understanding governance,risk and compliance information systems (GRC IS): The experts view

Although Governance, Risk and Compliance (GRC) is an emerging field of study within the information systems (IS) academic community, the concept behind the acronym has to still be demystified and further investigated. The study investigates GRC systems in depth by (a) reviewing the literature on existing GRC studies, and (b) presenting a field study on views about GRC application by professional experts. The aim of this exploratory study is to understand the aspects and the nature of the GRC system following an enterprise systems approach. The result of this study is a framework of particular GRC characteristics that need to be taken into consideration when these systems are put in place. This framework includes specific areas such as: goals and objectives, purpose of the system, key stakeholders, methodology and requirements prior to implementation, critical success factors and problems/barriers. Further discussion about the issues, the concerns and the diverse views on GRC would assist in developing an agenda for the future research on the GRC field.     

Drivers of mobile payment acceptance: The impact of network externalities

Mobile payment is an attractive option that has recently boomed because of the advent of smart phones and their applications. Despite the great potential of such technology in simplifying our lives, its uptake remains limited. As the technology acceptance fails to meet expectations, this study aims at providing a better understanding of the factors influencing mobile payment acceptance. Through an empirical investigation that couples the traditional technology acceptance factors with “network externalities” effect. This study hypothesized that performance expectancy, effort expectancy; social influence, trust, and network externality are major factors that influence the intention to use mobile payment. Results indicated that while the traditional acceptance drivers still impact customers’ willingness to adopt mobile payment, network externalities was the most influential driver of mobile payment acceptance. Results also failed to support the influence of effort expectancy. Conclusions and future work propositions are stated at the end.     

Two CEGAR-based approaches for the safety verification of PLC-controlled plants

In this paper we address the safety analysis of chemical plants controlled by programmable logic controllers (PLCs). We consider a specification of the control program of the PLCs, extended with the specification of the dynamic plant behavior. The resulting hybrid models can be transformed to hybrid automata, for which advanced techniques for reachability analysis exist. However, the hybrid automata models are often too large to be analyzed. We propose two counterexample-guided abstraction refinement (CEGAR) approaches to keep the size of the hybrid models moderate.     

Standard goal programming with fuzzy hierarchies: a sequential approach

This paper proposes a pragmatic model for multi-objective decision-making processes involving clusters of objectives which have a decisional meaning for the decision maker (DM). We provide the DMs with a comfortable tool that allows them to express their preferences both by comparing criteria of the same cluster and via the comparison between the different clusters. In standard goal programming the importance of the goals is modeled by the introduction of preferential weights or/and the incorporation of pre-emptive priorities. However, in many cases the DM is not able to establish a precise preference structure. Even in the case of precise weights the solution does not match necessarily the relative weights or, in the case of precise pre-emptive priority, the result could be very restrictive. In order to overcome these drawbacks, in this paper the normalized unwanted deviations are interpreted in terms of achievement degrees of the goals and fuzzy relations are used to model the relative importance of the goals. Thus, we show how several methodologies from the fuzzy goal programming literature can be tailored for solving standard GP problems. We apply this new modeling to problems where there is a “natural” clustering between goals of the same class. We address this situation by solving two phases; in the first one each class is handled separately taking into account the hierarchy of their goals and, in the second phase, we integrate the results of the first phase and the imprecise hierarchy of the different classes. We formulate a new goal programming model called as sequential goal programming with fuzzy hierarchy model. Because many real situations involve decision making in this environment, our proposal can be a useful tool of broad application. A numerical example illustrates the methodology.     

Image processing acceleration for intelligent unmanned aerial vehicle on mobile GPU

In this paper, we present an algorithm for providing visually-guided unmanned aerial vehicle (UAV) control using visual information that is processed on a mobile graphic processing unit (GPU). Most real-time machine vision applications for UAVs exploit low-resolution images because the shortage of computational resources comes from size, weight and power issue. This leads to the limitation that the data are insufficient to provide the UAV with intelligent behavior. However, GPUs have emerged as inexpensive parallel processors that are capable of providing high computational power in mobile environments. We present an approach for detecting and tracking lines that use a mobile GPU. Hough transform and clustering techniques were used for robust and fast tracking. We achieved accurate line detection and faster tracking performance using the mobile GPU as compared with an x86 i5 CPU. Moreover, the average results showed that the GPU provided approximately five times speedup as compared to an ARM quad-core Cortex-A15. We conducted a detailed analysis of the performance of proposed tracking and detection algorithm and obtained meaningful results that could be utilized in real flight.