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Identifying Native Applications with High Assurance
Get PDFThe work described in this paper investigates the problem
of identifying and deterring stealthy malicious processes on
a host. We point out the lack of strong application iden-
tication in main stream operating systems. We solve the
application identication problem by proposing a novel iden-
tication model in which user-level applications are required
to present identication proofs at run time to be authenti-
cated by the kernel using an embedded secret key. The se-
cret key of an application is registered with a trusted kernel
using a key registrar and is used to uniquely authenticate
and authorize the application. We present a protocol for
secure authentication of applications. Additionally, we de-
velop a system call monitoring architecture that uses our
model to verify the identity of applications when making
critical system calls. Our system call monitoring can be
integrated with existing policy specication frameworks to
enforce application-level access rights. We implement and
evaluate a prototype of our monitoring architecture in Linux
as device drivers with nearly no modication of the ker-
nel. The results from our extensive performance evaluation
shows that our prototype incurs low overhead, indicating the
feasibility of our model
GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection
Get PDFLocal based approach is a major category of methods for spatial outlier detection (SOD). Currently, there is a lack of systematic analysis on the statistical properties of this framework. For example, most methods assume identical and independent normal distributions
(i.i.d. normal) for the calculated local differences, but no justifications for this critical assumption have been presented. The methods’ detection performance on geostatistic data with linear or nonlinear trend is also not well studied. In addition, there is a lack of theoretical connections and empirical comparisons between local and global based SOD approaches. This paper discusses all these fundamental issues under the proposed generalized local statistical (GLS) framework. Furthermore, robust estimation and outlier detection methods are designed for the new GLS model. Extensive simulations demonstrated that the SOD method based on the GLS model significantly outperformed all existing approaches when the spatial data exhibits a linear or nonlinear trend
Space-time adaptive solution of inverse problems with the discrete adjoint method
Get PDFAdaptivity in both space and time has become the norm for solving problems modeled by partial differential equations. The size of the discretized problem makes uniformly refined grids computationally prohibitive. Adaptive refinement of meshes and time steps allows to capture the phenomena of interest while keeping the cost of a simulation tractable on the current hardware. Many fields in science and engineering require the solution of inverse problems where parameters for a given model are estimated based on available measurement information. In contrast to forward (regular) simulations, inverse problems have not extensively benefited from the adaptive solver technology. Previous research in inverse problems has focused mainly on the continuous approach to calculate sensitivities, and has typically employed fixed time and space meshes in the solution process. Inverse problem solvers that make exclusive use of uniform or static meshes avoid complications such as the differentiation of mesh motion equations, or inconsistencies in the sensitivity equations between subdomains with different refinement levels. However, this comes at the cost of low computational efficiency. More efficient computations are possible through judicious use of adaptive mesh refinement, adaptive time steps, and the discrete adjoint method.
This paper develops a framework for the construction and analysis of discrete adjoint sensitivities in the context of time dependent, adaptive grid, adaptive step models. Discrete adjoints are attractive in practice since they can be generated with low effort using automatic differentiation. However, this approach brings several important challenges. The adjoint of the forward numerical scheme may be inconsistent with the continuous adjoint equations. A reduction in accuracy of the discrete adjoint sensitivities may appear due to the intergrid transfer operators. Moreover, the optimization algorithm may need to accommodate state and gradient vectors whose dimensions change between iterations. This work shows that several of these potential issues can be avoided for the discontinuous Galerkin (DG) method. The adjoint model development is considerably simplified by decoupling the adaptive mesh refinement mechanism from the forward model solver, and by selectively applying automatic differentiation on individual algorithms.
In forward models discontinuous Galerkin discretizations can efficiently handle high orders of accuracy, -refinement, and parallel computation. The analysis reveals that this approach, paired with Runge Kutta time stepping, is well suited for the adaptive solutions of inverse problems. The usefulness of discrete discontinuous Galerkin adjoints is illustrated on a two-dimensional adaptive data assimilation problem
Enrichment Procedures for Soft Clusters: A Statistical Test and its Applications
Get PDFClusters, typically mined by modeling locality of attribute spaces, are often evaluated for their ability to demonstrate ‘enrichment’ of categorical features. A cluster enrichment procedure evaluates the membership of a cluster for significant representation in pre-defined categories of interest. While classical enrichment procedures assume a hard clustering deï¬nition, in this paper we introduce a new statistical test that computes enrichments for soft clusters. We demonstrate an application of this test in reï¬ning and evaluating soft clusters for classification of remotely sensed images
Exploring the Impact of Context Sensitivity on Blended Analysis
Get PDFThis paper explores the use of context sensitivity both intra- and interprocedurally in a blended (static/dynamic) program analysis for performance diagnosis of framework-intensive Web-based applications. Empirical experiments with an existing blended analysis algorithm [9] compare combinations of (i) use of a context-insensitive call graph with a context-sensitive calling context tree, and (ii) use (or not) of context-sensitive code pruning within methods. These experiments demonstrate achievable gains in scalability and performance in terms of several metrics designed for blended escape analysis, and report results in terms of object instances created, to allow more realistic conclusions from the data than were possible previously
Mobilizar: Capturing User Behavior with Mobile Digital Diaries
Get PDFIn this paper we present Mobilizar, a web-based mobile tool that facilitates the implementation and data collection of self-reported user behavior. Mobilizar was designed with both the researcher and the participant in mind. It provides investigators with a way to setup a new diary study in a matter of minutes and to electronically collect diary data from participants by using internet-enabled mobile devices. These devices promise to alleviate the burden of carrying a paper-and-pencil diary by instead using the participant’s own device. It also gives participants the flexibility to report their behavior in different ways such as making text, voice, or picture entries that fit their current situational constraints. In this paper, we describe the user interface design of Mobilizar and how it may be used to conduct diary studies with mobile devices
The Family Window: Perceived Usage and Privacy Concerns
Get PDFFamilies have a strong need to connect with their loved ones over distance. However, most technologies do not provide the same feelings of connectedness that one feels from seeing remote family members. Hence our goal was to understand if a video connection, in the form of a media space, could help families feel more connected. To answer this, we designed a video media space called the Family Window and deployed it within the homes of two families for eight months and four families for five weeks. We also interviewed 16 individuals to obtain additional feedback about the system and to learn about their privacy concerns
Revision of TR-09-25: A Hybrid Variational/Ensemble Filter Approach to Data Assimilation
Get PDFTwo families of methods are widely used in data assimilation: the
four dimensional variational (4D-Var) approach, and the ensemble Kalman filter
(EnKF) approach. The two families have been developed largely through parallel
research efforts. Each method has its advantages and disadvantages. It is of
interest to develop hybrid data assimilation
algorithms that can combine the relative strengths of the two approaches.
This paper proposes a subspace approach to investigate the theoretical equivalence between the suboptimal
4D-Var method (where only a small number of optimization iterations are
performed) and the practical EnKF method (where only a small number of ensemble
members are used) in a linear Gaussian setting. The analysis motivates a new
hybrid algorithm: the optimization directions obtained from a short window
4D-Var run are used to construct the EnKF initial ensemble.
The proposed hybrid method is computationally less expensive than a full
4D-Var, as only short assimilation windows are considered. The hybrid method has the potential to
perform better than the regular EnKF due to its look-ahead property.
Numerical results
show that the proposed hybrid ensemble filter method performs better than the
regular EnKF method for both linear and nonlinear test problems
CampProf: A Visual Performance Analysis Tool for Memory Bound GPU Kernels
Get PDFCurrent GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge these performance models, by modeling and analyzing a lesser known, but very severe performance pitfall, called 'Partition Camping', in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of memory-bound CUDA kernels by up to seven-times. No existing tool can detect the partition camping effect in CUDA kernels.
We complement the existing tools by developing 'CampProf', a spreadsheet based, visual analysis tool, that detects the degree to which any memory-bound kernel suffers from partition camping. In addition, CampProf also predicts the kernel's performance at all execution configurations, if its performance parameters are known at any one of them. To demonstrate the utility of CampProf, we analyze three different applications using our tool, and demonstrate how it can be used to discover partition camping. We also demonstrate how CampProf can be used to monitor the performance improvements in the kernels, as the partition camping effect is being removed.
The performance model that drives CampProf was developed by applying multiple linear regression techniques over a set of specific micro-benchmarks that simulated the partition camping behavior. Our results show that the geometric mean of errors in our prediction model is within 12% of the actual execution times. In summary, CampProf is a new, accurate, and easy-to-use tool that can be used in conjunction with the existing tools to analyze and improve the overall performance of memory-bound CUDA kernels
Storytelling Security: User-Intention Based Traffic Sanitization
Get PDFMalicious software (malware) with decentralized communication infrastructure, such as peer-to-peer botnets, is difficult to detect. In this paper, we describe a traffic-sanitization method for identifying malware-triggered outbound connections from a personal computer. Our solution correlates user activities with the content of outbound traffic. Our key observation is that user-initiated outbound traffic typically has corresponding human inputs, i.e., keystroke or mouse clicks. Our analysis on the causal relations between user inputs and packet payload enables the efficient enforcement of the inter-packet dependency at the application level.
We formalize our approach within the framework of protocol-state machine. We define new application-level traffic-sanitization policies that enforce the inter-packet dependencies. The dependency is derived from the transitions among protocol states that involve both user actions and network events. We refer to our methodology as storytelling security.
We demonstrate a concrete realization of our methodology in the context of peer-to-peer file-sharing application, describe its use in blocking traffic of P2P bots on a host. We implement and evaluate our prototype in Windows operating system in both online and offline deployment settings. Our experimental evaluation along with case studies of real-world P2P applications demonstrates the feasibility of verifying the inter-packet dependencies. Our deep packet inspection incurs overhead on the outbound network flow. Our solution can also be used as an offline collect-and-analyze tool