Evaluating the effectiveness of pointer alias analyses

The fast spread of a worm is a great challenge to Internet security. Most current defense systems use a signature matching approach while most signatures are developed manually. It is difficult to catch a variety of new worms promptly. An efficient worm defense system is designed and implemented to provide early warning at the moment the worms start to spread in the network and to contain or slow down the spread of the worm by automatically extracting a signature that could be used by firewalls or Intrusion Prevention Systems. Several recent efforts to automatically extract worm signatures from Internet traffic have been done, but the efficiency is an unsolved problem especially in real high-speed network. In this paper, we proposed an efficient worm defense system based signature extraction. The input of the system is all traffic crossing an edge network and its output is a database of worm signatures which can be used by content-based defense. There are three main stages to extract signatures from network traffic. First, a clustering stage uses multidimensional traffic mining based IP header to identify significant traffic volume. We propose a binary clustering algorithm and this leaves a preferred policy to improve the front traffic filter, which can reduce the traffic to be processed and enhance its purity. After clustering, nonsignificant traffic volume is stored in an innocuous packet pool and significant traffic volume is further classified using address dispersion as suspicious or innocuous. After this stage, only a small portion of the packets captured from the edge network are analyzed in third stage, signature extraction. A position-aware signature generation method based bloom filter is proposed to extract more accurate signatures with less CPU time and memory consumption. To minimize false positives, the signatures will be verified based on the innocuous packet pool. Both trace data and tcpdump data are used to test the prototype system. Experiment results show that the system can efficiently filter through suspicious traffic with high purity and extract more accurate signature, which can well support popular content-based defense system such as Snort.

We develop a direct finite-element solver of linear (optimal) complexity to extract broadband circuit parameters such as S-parameters of arbitrarily shaped 3-D interconnects in inhomogeneous dielectrics. Numerical experiments demonstrate a clear advantage of the proposed solver as compared with existing finite-element solvers that employ state-of-the-art direct sparse matrix solutions. A linear complexity in both CPU time and memory consumption is achieved with prescribed accuracy satisfied. A finite-element matrix from the analysis of a large-scale 3-D circuit in multiple dielectrics having 5.643 million unknowns is directly factorized in less than 2 hours on a single core running at 2.8 GHz.

Event detection over RFID event streams is one of the most important applications of RFID based monitoring systems. Event detection over data stream extracts meaningful complex patterns from high volume, fast speed and real time raw event streams. This research aims to detect RFID event patterns by introducing context information to reduce event instances effectively. Context of an RFID application is modeled and merged into event query optimization framework. Context information is further applied into evaluation of pattern detection. Experimental results show that context information can optimize complex event detection process both on CPU time and memory consumption.

In this work the resonant frequencies in spheroidal metallic cavities loaded with isotropic and anisotropic material, are studied. For the case of isotropic infills, an extended bound-ary condition integral equation method (referred hereafter as extended integral equation) is employed while, a local point tech-nique is developed for anisotropic loadings. The anisotropy used in this work is of uniaxial type while prolate and oblate spheroidal cavities are studied. The extended integral equation is based on numerical integration of the involved special functions along the generating curve of the spheroidal surface. On the contrary, the local point technique satisfies the requisite boundary condition at a predefined set of points on the generating curve without the necessity to apply integration techniques. The emerging hybrid modes are examined and discussed. In particular, the spheroidal cavity spectrum for isotropic infills is extracted and compared by both the extended integral equation and the local point technique. Concerning uniaxial loaded cavities, the resonant frequencies are calculated solely by the local point technique. To validate these results, we utilize the HFSS commercial software. The local point technique turns out to be computationally efficient in terms of CPU time and memory consumption, as compared to the commercial solver. Various numerical results are presented and discussed.

For a variety of multiscale electromagnetic (EM) problems, mesh densities that are more than sufficient for the frequency of interest have to be adopted in order to maintain the integrity of multiscale geometrical features. The adoption of the overly fine mesh, or locally overly fine mesh, would render the multi-level fast multiple method (MLFMM) inefficient during the matrix-vector multiplication (MVP), both in terms of CPU time and memory consumption. Similar issue occurs also in wideband analyses. In this paper, we proposed a hierarchical multi-level fast multipole method (H-MLFMM) based on the skeletonalization technique to address effectively the EM scattering from targets with multiscale features. Numerical examples demonstrate the accuracy and applicability of the H-MLFMM algorithm.

Many program analyses benefit, both in precision and performance, from precise pointer analysis. An important dimension of pointer analysis precision is flow-sensitivity, which has been shown to be useful for applications such as program verification and static analysis of binary code, among many others. However, flow-sensitive pointer analysis has historically been unable to scale to programs with millions of lines of code. We present a new flow-sensitive pointer analysis algorithm that is an order of magnitude faster than the existing state of the art, enabling for the first time flow-sensitive pointer analysis for programs with millions of lines of code. Our flow-sensitive algorithm is based on a sparse representation of program code created by a staged, flow-insensitive pointer analysis. We explain how this new algorithm is a member of a new family of pointer analysis algorithms that deserves further study.

Based on current-domain large-signal equivalent circuits, a new physical timing model for bipolar nonthreshold logic (NTL) circuits is developed. Through extensive comparisons with SPICE simulation results, it is found that the maximum error is 25% for the NTL inverters with different operating currents, capacitance loads, device parameters, and input excitation waveforms not deviating much from characteristic waveforms. Moreover, the consumed CPU time and memory in calculations are much less than those in full transient simulations. >

At each program point, points-to analysis for statically typed object oriented programming languages (e.g., Java, C++) determines those objects to which a reference may refer (or a pointer may point) during execution. Points-to analysis is necessary for any semantics based software tools for object oriented systems. Our new complexity results for points-to analysis distinguish the difficulty of intraprocedural and interprocedural points-to analyses for languages with combinations of single-level types (i.e., types with data members only of primitive type), exceptions with or without subtyping, and dynamic dispatch. Our results include: 1) the first polynomial-time algorithm for points-to analysis in the presence of exceptions that handles a robust subset of Java without threads and can be applied to C++; 2) proof that the above algorithm is safe, in general, and provably precise on programs with single-level types and exceptions without subtyping, but not dynamic dispatch, thus, this case is in P; 3) proof that an interprocedural points-to analysis problem with single-level types and exceptions with subtyping, but without dynamic dispatch, is PSPACE-hard, while the intraprocedural problem is PSPACE-complete. Other complexity characterizations of points-to analysis in programs without exceptions are presented, including an algorithm with worst-case bound of O(n/sup 5/), which improves over the O(n/sup 7/) worst-case bound achievable from previous approaches of T. Reps et al. (1995) and W.A. Landi and B.G. Ryder (1991).

Today automatic meshing of CAD geometry is the most common method of FEM mesh generation. However, to get results of acceptable accuracy with universal meshing algorithms it is necessary to use rather small-sized elements which leads to high memory and CPU time consumption. Furthermore, the irregularity of automatically generatated meshes makes it difficult to create well-defined local areas with different material properties. A solution for this problem is the application of predefined building blocks (FEM-Features) which contain algorithms for a problem-adapted mesh generation. These building blocks may be realised as in an object-oriented modelling system. By employing interface objects, it is easy to combine design objects to form complex geometries. The technique is explained by an example.

This paper describes the activities carried out for the development and the use of CRFD codes and related procedures for the design of industrial furnaces. The objective is to predict the emission of pollutants, such as nitrogen oxides, in combustion flue gases. This prediction may be correctly accomplished using a detailed kinetic mechanism, which cannot be directly implemented on CRFD codes due to actual computational limits (both in terms of memory and CPU time consumption). A new approach has been developed: analysing the 3D CRFD flow fields, an “equivalent” chemical reactors network model is extracted with corresponding residence time distributions and overall reactor properties, and the detailed kinetic calculation is performed on this simpler scheme. The approach has been successfully applied to different scale of furnaces such as pilot plants and industrial boilers, low- NOx burners and glass furnaces. In the papers a description of the CRFD codes is given, and the methodology to extract a chemical ideal reactor network from CFRD fields is presented. Finally, an application of the procedure on the Monfalcone #3 steam generator is discussed.

The windowing technique was introduced in 1993 by Heinemann and Deimbacher (Ref 9). This method allows a locally restricted and time-dependent replacement of grids and parameters during simulation runs. Windows can represent any area of special interest in a reservoir. In most cases this will be the near wellbore area, in order to resolve near wellbore effects and to honor the pattern of flow. Introducing a wellblock with exactly the same dimensions as the real wellbore and gridding the near wellbore area allows to model all sorts of well trajectories, by applying the 3D (k-)PEBI grid method. Introducing small block sizes requires a drastic reduction of the timestep length. Therefore the windowing technique can only be applied if the solution of the equations for the full model and the solution of the window area is decoupled. This can be done based on a special kind of Domain Decomposition. In a first step the fully implicit solution for the full-scale model will be calculated but the inner blocks of the windows are solved for the pressure only, without updating the saturations and mole fractions, providing the boundary influx for the windows, which are then solved for the same overall time step with up to 1000 small local steps. The windowing technique can find numerous applications. Keeping overall CPU time consumption in he order of magnitude of a conventional model, the windowing technique is a potential approach to replace the analytical Peaceman (Ref. 23) model in full field reservoir simulation, while increasing the quality of the results. A further application of this technique is the simulation of well tests in full field reservoir models. This paper presents the advances in the gridding and dynamic solution process. Several examples to demonstrate the advantages of this technique for detail - and full field reservoir simulation will be included. Introduction Special treatment and high resolution, both in time and space, of regions of interest, as for example the near wellbore area, has always been an important question in reservoir simulation. But using small timesteps and local grid refinement unavoidably lead to long and uneconomical CPU times. In the attempt to solve this problem several techniques have been developed in the reservoir simulation literature, such as patch refinement. true refinement or multigrid methods (Ref. 2–6), but none of these approaches could fully satisfy the requirements of local grid replacement by flexible and irregular grids and its time dependent incorporation in full field reservoir simulation. In 1986 Aziz and Pedrosa introduced the concept of "hybrid grids" in a Cartesian block system (Ref. 1), trying to overcome the insufficiencies of the Peaceman model. Gridding the near wellbore area by hybrid grids honors the radial nature of flow, small block sizes help handling large saturation changes caused by high production rates. The paper showed that the solution of the equations in the radially gridded well region can be decoupled from the solution of the equations of the conventional grid system by either applying an iterative or a direct solution method.

Short term daily flow prediction for tropical watersheds characterised by seasonal rain was carried out for Aliyabad watershed in Tamilnadu, India. Wiener Multiple time series approach and Venkataraman autoregressive approach were carried out for earlier data and analysis for a period of 14 years from 1958 to 1971 in 1978 and recent data and analysis of 17 years from 1972 to 1988 in 1991. C programs were developed for Fortran programs developed earlier. The major result of the study was the confirmation of the better behaviour of the raw time series in contrast to that of the decomposed time series signifying separately its trend,periodicity,persistence and the random residual components. Also it was observed in confirmity with the earlier work that no significant improvement was achieved by increasing the order of the model from 2 to 4 in the Venkataraman approach and that the error variances were less for southwest monsoon as compared to northeast monsoon prediction analysis. The complexity of rainfall prediction was also observed as compared to flow prediction. In the Wiener approach the error variances for southwest monsoon analysis were close for daily flows than for weekly flows. For prediction of daily rain and weekly flows Venkataraman approach yielded better results. In C programming the CPU time consumption was 96 seconds for both Wiener and Venkataraman approaches. Individually Venkataraman approach consumed 47 seconds and Wiener approach consumed 61 seconds, the total time being 108 seconds. For earlier data analysis in Fortran IV programming corresponding CPU time for Venkataraman approach was 62 seconds, for Wiener approach 75 seconds, the total time being 137 seconds.

In this paper, the heat transfer characteristics of natural convection around a vertical flat surface embedded in a saturated porous medium are studied by taking into account the thermal radiation effect. With the aid of the similarity transformations, the governing equations of the problem are reduced to the system of ordinary differential equations over the infinite domain. Moreover, the Legendre wavelet operational matrix method (LWOMM) is hired to find the numerical solution to the considered problem. Initially, to ensure the efficiency of the proposed strategy, a third-order nonlinear boundary value problem having the exact solution is considered a test problem. The numerical findings elucidate that the solutions obtained by the LWOMM are in good agreement with the exact solution as compared with the solution obtained by the Haar wavelet operational matrix method, with the consumption of less computational CPU time. Also, the temperature and velocity profiles are discussed graphically.

Pointer analysis is a prerequisite for many program analyses, and the effectiveness of these analyses depends on the precision of the pointer information they receive. Two major axes of pointer analysis precision are flow-sensitivity and context-sensitivity, and while there has been significant recent progress regarding scalable context-sensitive pointer analysis, relatively little progress has been made in improving the scalability of flow-sensitive pointer analysis.This paper presents a new interprocedural, flow-sensitive pointer analysis algorithm that combines two ideas-semi-sparse analysis and a novel use of BDDs-that arise from a careful understanding of the unique challenges that face flow-sensitive pointer analysis. We evaluate our algorithm on 12 C benchmarks ranging from 11K to 474K lines of code. Our fastest algorithm is on average 197x faster and uses 4.6x less memory than the state of the art, and it can analyze programs that are an order of magnitude larger than the previous state of the art.

How to detect and classify the huge malware samples received every day is a major challenge of security area. In recent years, using function call graph to detect and classify malicious software has become a feasible method. As the basic technology of call graph construction, function pointer analysis becomes more noticeable. Previous works often use the result of pointer analysis to determine the possible targets of function pointer calls. However, the inherent complexity and efficiency problem of the pointer analysis often leads to unsatisfactory results when applied to practical programs. This paper presents a strong connected component (SCC) level flow-sensitive and context-sensitive function pointer analysis algorithm (referred as FP algorithm). This algorithm not only makes up for the speed deficiency of pointer analysis, but also obtains higher precision. Measurements for 8 practical C programs show that FP algorithm advances 42.6 times on average compared with DSA algorithm and the precision is also improved.