41699961
Automated Detection of Code Vulnerabilities Based on Program Analysis and Model Checking
Ensuring the correctness and reliability of software systems is one of the main problems in software development. Model checking, a static analysis method, is preponderant in improving the precision of vulnerabilities detection. However, when applied to buffer overflow and other bugs, it is hard to automatically construct the model for detecting the vulnerabilities. To address this problem we propose an approach that combines constraint based analysis and model checking together. We trace the memory size of buffer-related variables and instrument the code with corresponding constraint assertions before the potential vulnerable points by constraint based analysis. Then the problem of detecting vulnerabilities is converted into the problem of detecting vulnerabilities to verifying the reach ability of these assertions by model checking. In order to reduce the cost of model checking, program slicing is introduced to reduce the code size. CodeAuditor is a prototype implementation of our approach. With CodeAuditor, several yet unreported vulnerabilities are discovered in several open source software, and the performance is shown to be improved significantly with the help of program slicing.
29918508
Round-Robin with VirtualClock Scheduling Algorithm in Multiservice Packet Networks
In this paper, we present a scheduler that incorporates round robin service within a VirtualClock discipline. Time-stamp based scheduling algorithms attain a low local delay bound and performance guarantee, but are computationally complex. On the other hand, round robin schemes are simple to implement and have computational complexity of O(1) but they are well known for their output burstiness and short-term unfairness. In order to overcome this problem, we combine round robin with VirtualClock in an algorithm we call VCRR. VCRR possesses better fairness than simple round robin, low jitter and a good scheduling delay bound. At the same time, VCRR preserves the O(1) time complexity of round robin. Simulation experiments show VCRR's efficiency in terms of delay performance, jitter and fairness..
15438049
DECF: A Coarse-Grained Data-Parallel Programming Framework for Seismic Processing
In this paper, we present a distributed execution control framework (DECF) for seismic processing. The DECF framework employs a coarse-grained data-parallel execution model within a cluster environment and achieves high throughput. The DECF schedules processing modules written as sequential programs to run simultaneously on multiple computers, or multiple CPU cores. The DECF hides the messy details of scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between processing modules.We also introduce a parallel implementation of 3D pre-stack kirchhoff time migration algorithm using MPI and share the practical lessons learned from the process of designing, implementing, and maintaining it, which are of great help to the future work of the DECF.
14387828
Escape analysis for synchronization removal
In this paper we introduce our escape analysis framework for Java, which is a kind of flow-insensitive, inter-procedural, and context-sensitive data flow analysis. And we present an efficient static intra-procedural algorithm for inferring the set of types that can occur at runtime for each object. With intra-procedural type information, virtual method resolution, callee-stub inline, exception analysis, and thread allocation site analysis are implemented. Virtual method resolution and callee-stub inline effectively reduce the call graph and SCC (strongly connected components). In exception analysis, we present a more precise analysis algorithm, especially in the inter-procedural exception in SCC.Our escape analysis can be applied to the whole Java program and Java libraries to identify unnecessary synchronization in Java and remove them. We have developed an implementation on Intel's Open Runtime Platform. For our benchmarks, 7.3% to 99% (with an average of 54%) of synchronization operations are eliminated.
15857630
Eliminating Redundant Communication of Code Generation for Distributed Memory Machines
How to eliminate redundant communications is a key issue in developing parallelizing compilers for distributed memory machines. An important way to improve the performance of the code generated by parallelizing compiler in distributed memory systems is to eliminate as much communications as possible. This paper investigates algorithms for generating communications code from previously published work and modifies them to eliminate redundant communications. Based on the mathematical model of symbolic coefficient linear inequalities the revisited formal specification of communications sets are presented and the corresponding improved algorithms are given. Experimental results indicate that the new algorithms can reduce communication messages and improve the performance of parallelized code significantly.
8970759
Linux kernels as complex networks: A novel method to study evolution
In recent years, many graphs have turned out to be complex networks. This paper presents a novel method to study Linux kernel evolution — using complex networks to understand how Linux kernel modules evolve over time. After studying the node degree distribution and average path length of the call graphs corresponding to the kernel modules of 223 different versions (V1.1.0 to V2.4.35), we found that the call graphs of the file system and drivers module are scale-free small-world complex networks. In addition, both of the file system and drivers module exhibit very strong preferential attachment tendency. Finally, we proposed a generic method that could be used to find major structural changes that occur during the evolution of software systems.
15085849
A novel framework for SVM-based image retrieval on large databases
In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image retrieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up testing further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.
23743946
Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model
Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.
2945869
Feature Subset Selection for Multi-class SVM Based Image Classification
Multi-class image classification can benefit much from feature subset selection. This paper extends an error bound of binary SVMs to a feature subset selection criterion for the multi-class SVMs. By minimizing this criterion, the scale factors assigned to each feature in a kernel function are optimized to identify the important features. This minimization problem can be efficiently solved by gradient-based search techniques, even if hundreds of features are involved. Also, considering that image classification is often a small sample problem, the regularization issue is investigated for this criterion, showing its robustness in this situation. Experimental study on multiple benchmark image data sets demonstrates the effectiveness of the proposed approach.
368510
A Performance Model for Domino Mail Server
Using performance model to analyze the Internet service is prevailing. However, the research of the performance model for mail server retrains in the immature status. In this paper, based on the queue theory, we propose a performance model for Domino mail server. The verification and analysis of the model are also presented. The experimental results show that this model characterizes the behavior of Domino mail server well and prove that it is useful for the qualitative performance measurement of Domino mail server.
38516500
A New Approach of Network Intrusion Detection Using HVDM-Based SOM
The research of applying Self-organizing Maps for intrusion detection is investigated in this paper. A novel approach is presented for enhancing SOM's abilities of identifying temporal network attacks, which combine with FIR filter. Meanwhile, we reconsider the heterogeneous dataset that composed of network connection's features, and select HVDM as the distance function determining the winning neuron during SOM's training and testing. In the end, KDD benchmark dataset is employed to validate the efficiency of our approach, and the results is detection rates of 96.5%, false positive rates of 6.2%, which accounts for good performance of our new approach in intrusion detection fields.