Starting from the 5.0 Lollipop release all Android processes must be run inside confined SEAndroid access control domains. As a result, Android device manufacturers were compelled to develop SEAndroid expertise in order to create policies for their device-specific components. In this paper we analyse SEAndroid policies from a number of 5.0 Lollipop devices on the market, and identify patterns of common problems we found. We also suggest some practical tools that can improve policy design and analysis. We implemented the first of such tools, SEAL.
Some researchers have applied information visualization techniques to SELinux policy analysis @cite_12 , also in combination with clustering @cite_14 . These techniques are also system-agnostic, and we may use them in future SEAndroid tools.
@cite_14 @cite_12
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Performing SELinux policy analyses can be difficult due to the complexity of the policy language and the sheer number of policy rules and attributes involved. For example, the default policy on most SELinux-enabled systems has over 1; 500; 000 flat rules, involving over 1; 780 types. Simple analyses between types can result in a large amount of data, which is poorly presented to administrators in existing analysis tools. We propose and implement a policy analysis tool “SEGrapher” that addresses the above challenges. SEGrapher visually presents analysis results as a simplified directed graph, where nodes are types, and edges are corresponding policy rules between types. Graphs are generated via a proposed clustering algorithm that clusters types based on their accesses. Clusters provide an abstraction layer that removes undesired data, and focuses on analysis attributes specified by the administrator. Analyzing and administrating system security policies is difficult as policies become larger and more complex every day. The paper present work toward analyzing security policies and sessions in terms of security properties. Our intuition was that combining both visualization tools that could benefit from the expert's eyes, and software analysis abilities, should lead to a new interesting way to study and manage security policies as well as users' sessions. Rather than trying to mine large and complex policies to find possible flaws within, work may concentrate on which potential flaws are really exploited by attackers. Actually, the paper presents some methods and tools to visualize and manipulate large SELinux policies, with algorithms allowing to search for paths, such as information flows within policies. The paper also introduces a complementary original approach to analyze and visualize real attack logs as session graphs or information flow graphs, or even aggregated multiple-sessions graphs. Our wishes is that in the future, when those tools will be mature enough, security administrator can then confront the statical security view given by the security policy analysis and the dynamical and real-world view given by the parts of attacks that most often occurred. In this paper, we present a systematic way to determine the information flow security goals achieved by systems running a secure O S, specifically systems running Security-Enhanced Linux. A formalization of the access control mechanism of the SELinux security server, together with a labeled transition system representing an SELinux configuration, provides our framework. Information flow security goal statements expressed in linear temporal logic provide a clear description of the objectives that SELinux is intended to achieve. We use model checking to determine whether security goals hold in a given system. These formal models combined with appropriate algorithms have led to automated tools for the verification of security properties in an SELinux system. Our approach has been used in other security management contexts over the past decade, under the name rigorous automated security management. SELinux can enforce a policy based on mandatory access control (MAC) for Linux so as to fulfill the security requirements of more strict access control for those hierarchical and classified management organizations or situations. But security policy configuration is a hard and intractable task for SELinux due to its recondite policy description language and huge number of complicated security policies. Thereafter, it is rather significant to build a powerful assistant tool to help security managers to perform security policy configurations. And the key problem is to analyze configured security policies automatically or semi-automatically so that as many configuration loopholes as possible can be detected and then be eliminated. In this paper, both security models and policy description language supported by SELinux are summarized at first. While methods for analyzing security policies are discussed in details secondly. Then a prototype for a tool to analyze security policies automatically is designed, implemented and tested as well as some preliminary test results are given. Finally, the research work in this paper is summed up and some further research directions are pointed out. In this paper, we present an approach for analyzing the integrity protection in the SELinux example policy. The SELinux example policy is intended as an example from which administrators customize to create a policy for their site's security goals, but the complexity of the model and size of the policy make this quite complex. Our aim is to provide an access control model to express site security goals and resolve them against the SELinux policy. Ultimately, we aim to define a minimal trusted computing base (TCB) that satisfies Clark-Wilson integrity, by first testing for the more restrictive Biba integrity policy and resolving conflicts using Clark-Wilson semantics. Our policy analysis tool, Gokyo, implements the following approach: (1) it represents the SELinux example policy and our integrity goals; (2) it identifies conflicts between them; (3) it estimates the resolutions to these conflicts; and (4) provides information for deciding upon a resolution. Using Gokyo, we derive a proposal for a minimal TCB for SELinux includes 30 subject types, and we identify the work remaining to ensure that TCB is integrity-protected. Our analysis is performed on the SELinux example policy for Linux 2.4.19. We present a novel implementation of the recently introduced information bottleneck method for unsupervised document clustering. Given a joint empirical distribution of words and documents, p(x, y), we first cluster the words, Y, so that the obtained word clusters, Ytilde;, maximally preserve the information on the documents. The resulting joint distribution. p(X, Ytilde;), contains most of the original information about the documents, I(X; Ytilde;) a I(X; Y), but it is much less sparse and noisy. Using the same procedure we then cluster the documents, X, so that the information about the word-clusters is preserved. Thus, we first find word-clusters that capture most of the mutual information about to set of documents, and then find document clusters, that preserve the information about the word clusters. We tested this procedure over several document collections based on subsets taken from the standard 20Newsgroups corpus. The results were assessed by calculating the correlation between the document clusters and the correct labels for these documents. Finding from our experiments show that this double clustering procedure, which uses the information bottleneck method, yields significantly superior performance compared to other common document distributional clustering algorithms. Moreover, the double clustering procedure improves all the distributional clustering methods examined here. For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview , an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system “in the wild”, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of “exploring” a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview 's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology. Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets. A common strategy in analyzing and visualizing large and heterogeneous data is dividing it into meaningful subsets. Interesting subsets can then be selected and the associated data and the relationships between the subsets visualized. However, neither the extraction and manipulation nor the comparison of subsets is well supported by state-of-the-art techniques. In this paper we present Domino, a novel multiform visualization technique for effectively representing subsets and the relationships between them. By providing comprehensive tools to arrange, combine, and extract subsets, Domino allows users to create both common visualization techniques and advanced visualizations tailored to specific use cases. In addition to the novel technique, we present an implementation that enables analysts to manage the wide range of options that our approach offers. Innovative interactive features such as placeholders and live previews support rapid creation of complex analysis setups. We introduce the technique and the implementation using a simple example and demonstrate scalability and effectiveness in a use case from the field of cancer genomics. Distributed graph processing platforms have helped emerging application domains use commodity clusters and Clouds to analyze large graphs. Vertex-centric programming models like Google Pregel, and their subgraph-centric variants, specify data-parallel application logic for a single vertex or component that execute iteratively. The locality and balancing of components within partitions affects the performance of such platforms. We propose three partitioning strategies for a subgraph-centric model, and analyze their impact on CPU utilization, communication, iterations, and makespan. We analyze these using Breadth First Search and PageRank algorithms on powerlaw and spatio-planar graphs. They are validated on a commodity cluster using our GoFFish subgraph-centric platform, and compared against Apache Giraph vertex-centric platform. Our experiments show upto 8 times improvement in utilization resulting to upto 5 times improvement of overall makespan for flat and hierarchical partitioning over the default strategy due to improved machine utilization. Further, these also exhibit better horizontal scalability relative to Giraph. With the increasing availability of annotated multimedia data on the Internet, techniques are in demand that allow for a principled joint processing of different types of data. Multiview learning and multiview clustering attempt to identify latent components in different features spaces in a simultaneous manner. The resulting basis vectors or centroids faithfully represent the different views on the data but are implicitly coupled and they were jointly estimated. This opens new avenues to problems such as label prediction, image retrieval, or semantic grouping. In this paper, we present a new model for multiview clustering that extends traditional non-negative matrix factorization to the joint factorization of different data matrices. Accordingly, the technique provides a new approach to the joint treatment of image parts and attributes. First experiments in image segmentation and multiview clustering of image features and image labels show promising results and indicate that the proposed method offers a common framework for image analysis on different levels of abstraction. This book is the outcome of the Dagstuhl Seminar 13201 on Information Visualization - Towards Multivariate Network Visualization, held in Dagstuhl Castle, Germany in May 2013.The goal of this Dagstuhl Seminar was to bring together theoreticians and practitioners from Information Visualization, HCI, and Graph Drawing with a special focus on multivariate network visualization, i.e., on graphs where the nodes and or edges have additional (multidimensional) attributes. The integration of multivariate data into complex networks and their visual analysis is one of the big challenges not only in visualization, but also in many application areas. Thus, in order to support discussions related to the visualization of real world data, also invited researchers from selected application areas, especially bioinformatics, social sciences, and software engineering. The unique "Dagstuhl climate" ensured an open and undisturbed atmosphere to discuss the state-of-the-art, new directions, and open challenges of multivariate network visualization. Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable. We present a new method for clustering based on compression. The method does not use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the price of using the noncomputable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (ternary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics, we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis. Abstract Visual attributes, from simple objects (e.g., backpacks, hats) to soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification. In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework. Individual tasks are grouped after performing hierarchical clustering based on their correlation. The clusters of tasks are learned in a curriculum learning setup by transferring knowledge between clusters. The learning process within each cluster is performed in a multi-task classification setup. By leveraging the acquired knowledge, we speed-up the process and improve performance. We demonstrate the effectiveness of our method via ablation studies and a detailed analysis of the covariates, on a variety of publicly available datasets of humans standing with their full-body visible. Extensive experimentation has proven that the proposed approach boosts the performance by 4 –10 . Content analysis and clustering of natural language documents becomes crucial in various domains, even in public health. Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. Information should be gathered from a broader range of sources, including the Web which in turn requires more robust processing capabilities. To address this limitation, in this paper, we propose a new approach to detect public health events in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 62 and a recall of 75 evaluated using manually annotated, real-world data. Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or self-organizing map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results. Even the careful GPU programmer can inadvertently introduce data races while writing and optimizing code. Currently available GPU race checking methods fall short either in terms of their formal guarantees, ease of use, or practicality. Existing symbolic methods: (1) do not fully support existing CUDA kernels, (2) may require user-specified assertions or invariants, (3) often require users to guess which inputs may be safely made concrete, (4) tend to explode in complexity when the number of threads is increased, and (5) explode in the face of thread-ID based decisions, especially in a loop. We present SESA, a new tool combining Symbolic Execution and Static Analysis to analyze C++ CUDA programs that overcomes all these limitations. SESA also scales well to handle non-trivial benchmarks such as Parboil and Lonestar, and is the only tool of its class that handles such practical examples. This paper presents SESA's methodological innovations and practical results. Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations Visualizing large geo-demographical datasets using pixel-based techniques involves mapping the geospatial dimensions of a data point to screen coordinates and appropriately encod- ing its statistical value by color. The analysis of such data presents a great challenge. General tasks involve clustering, categorization, and searching for patterns of interest for sociological or economic research. Available visual encodings and screen space limitations lead to over-plotting and hiding of patterns and clusters in densely populated areas, while sparsely populated areas waste space and draw the attention away from the areas of interest. In this paper, two new approaches (RadialScale and AngularScale) are introduced to create density-equalized maps, while preserving recognizable features and neighborhoods in the visualization. These approaches build the core of a multi-scal- ing technique based on local features of the data described as local minima and maxima of point density. Scaling is conducted several times around these features, which leads to more homogeneous distortions. Results are illustrated using several real-world datasets. Our evaluation shows that the proposed techniques outperform traditional techniques as regard the homogeneity of the resulting data distributions and therefore build a more appropriate basis for analytic purposes. This paper presents a simple and robust method, namely discriminative histograms of local dominant orientation (D-HLDO), for biometric image feature extraction. In D-HLDO, the local dominant orientation map and the corresponding relative energy map are obtained by applying the singular value decomposition (SVD) to the collected gradient vectors over a local patch. The dominant orientation map and the relative energy map are then used to construct the concatenated histogram features. Local mean based nearest neighbor discriminant analysis (LM-NNDA) is finally employed to reduce the redundancy information and get the low-dimensional and discriminative features. The proposed method is applied to face, finger-knuckle-print and Palm biometrics and is examined using the AR, CMU PIE and FRGCv2.0 face image databases, the PolyU Palmprint database, and the PolyU Finger-Knuckle-Print database. Experimental results demonstrate the effectiveness of the proposed D-HLDO method.