Probabilistic Points-to Analysis for Java

Ahstract-Points-to analysis is a fundamental, but computationally intensive technique for static program analysis, optimization, debugging and verification. Context-Free Language (CFL) reachability has been proposed and widely used in demand-driven points-to analyses that aims for computing specific points-to relations on demand rather than all variables in the program. However, CFL-reachability-based points-to analysis still faces challenges when applied in practice especially for flow-sensitive points-to analysis, which aims at improving the precision of points-to analysis by taking account of the execution order of program statements. We propose a scalable approach named Parseeker to parallelize flow-sensitive demand-driven points-to analysis via CFL-reachability in order to improve the performance of points-to analysis with high precision. Our core insights are to (1) produce and process a set of fine-grained, parallelizable queries of points-to relations for the objective program, and (2) take a CFL-reachability-based points-to analysis to answer each query. The MapReduce is used to parallelize the queries and three optimization strategies are designed for further enhancing the efficiency.

Gwadar City is located at the coastline of Pakistan. The city is currently in a phase of development, which is expected to become a future economic hub for Pakistan. This has led us to choose Gwadar for seismic hazard evaluation. Seismic hazard analysis for Gwadar is carried out using deterministic and probabilistic seismic hazard analysis techniques. The present study will help in sustainable development of a future large city and economic hub for Pakistan on ways of coping from a major threat of earthquake hazard. In deterministic seismic hazard analysis, line sources were identified close to Gwadar. Based on the analysis of maximum magnitude and closest distance (worse conditions), Makran subduction zone was identified out of all the line sources with earthquake potential of 8.2 at a distance of 30 km. This yielded a peak ground acceleration value of 0.38 g for Gwadar City. In second phase, probabilistic seismic hazard analysis technique with the area source modeling was adopted to acquire results at different return periods. For this purpose, seismic data were collected from the Pakistan Meteorological Department and International Seismological Center (2010) databases for development of a comprehensive data catalog. The a and b values were obtained using regression analysis for each source zone, and probabilistic analysis yielded the results of 0.34 g for a return period of 500 years. As per building codes of Pakistan, areas or cities with ground acceleration greater than 0.32 g are considered in seismic zone 4, and both deterministic and probabilistic hazard analysis place the city in seismic zone 4. These values correspond to rock site with shear wave velocity of 760 m/s.

In recent years, random caches have been proposed as a way to simplify the timing analysis of real-time systems. However, technology-scaling makes caches prone to faults. Fault detection mechanisms can detect permanent faults but they affect the timing analysis of a random cache. This paper introduces a Static Probabilistic Timing Analysis (SPTA) technique that accounts for a permanent fault detection mechanism. The permanent fault detection mechanism periodically checks caches for faults and disables faulty cache blocks to prevent future accesses. The SPTA method operates by periodically switching its runtime between the fault-detection and the no-fault-detection states. This is the first SPTA with a realistic permanent fault detection mechanism. Experiments show that the proposed method always provides safe timing estimations—even when few memory blocks are provided—and accurate results—when sufficient memory blocks are present.

This research investigated the reliability of a newly designed steel roof truss system of an industrial building to be constructed in one of the major cities in Nigeria. The probabilistic analysis technique was done with the aid of CalREL, a general-purpose structural reliability analysis software program. The longest span truss element (consisting of 73 members), is the most critical in the system, was selected and first analysed using SAP2000 Advanced 12.0.0 finite element analysis (FEA) software program in order to obtain the forces in the steel truss members; this forms part of the inputs required in CalREL. Four load variations (referred to as load ratios in the study) were tested on the selected truss. The strengths of the truss members and other properties were determined as specified in BS 5950-1: 2000. Limit state equations were derived for the calculation of the probability of failure of the individual members of the truss system. A reliability index as a measure of structural performance and related to the probability of failure was developed for all the elements of the truss. The results showed that compression members displayed a noticeable violation of the ultimate limit state requirement, while tension members showed a negligible violation. Sensitivity factors that reflect the relative importance of the individual variables in the design of roof trusses were also presented. The estimated reliability indices also revealed structural members that require immediate redesign; though they appear satisfactory in the level of deterministic design. A probabilistic approach for the reappraisal of new and existing civil structures is well supported by the findings of this investigation.

This survey covers probabilistic timing analysis techniques for real-time systems. It reviews and critiques the key results in the field from its origins in 2000 to the latest research published up to the end of August 2018. The survey provides a taxonomy of the different methods used, and a classification of existing research. A detailed review is provided covering the main subject areas: static probabilistic timing analysis, measurement-based probabilistic timing analysis, and hybrid methods. In addition, research on supporting mechanisms and techniques, case studies, and evaluations is also reviewed. The survey concludes by identifying open issues, key challenges and possible directions for future research.

Static and dynamic program analysis techniques are important research areas in software security. Static analysis helps us locate vulnerabilities in a software by looking at the source code. Dynamic analysis helps us reason about the behavior of the software from information gathered at run-time. In this thesis, we are focusing on data flow analysis and heap analysis which are key static and dynamic program analysis techniques respectively.
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\nIn the first part of this thesis, we aim at detecting vulnerabilities in Android applications which have capability leaks. The security of the Android platform relies mainly on sandboxing applications and restricting their capabilities such that no application, by default, can perform any operations that would adversely impact other applications, the operating system, or the user. However, a recent research reported that a genuine but vulnerable application may leak its capabilities. When being leveraged, other applications can gain extra capabilities which they are not granted originally. We present DroidChecker, an Android application analyzing tool which searches for the aforementioned vulnerability in Android applications. DroidChecker uses interprocedural control flow graph searching and static taint checking to detect exploitable data paths in an Android application. We analyzed more than 1100 Android applications using DroidChecker and found 6 previously unknown vulnerable applications including the renowned Adobe Photoshop Express application. We also developed a malicious application that exploits the previously unknown vulnerability found in the Adobe Photoshop Express application. We showed that the malicious application, which is not granted any permissions, can access contacts on the phone with just a few lines of code.
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\nIn the second part of this thesis, we explore the use of heap analysis to extract software birthmarks. There are techniques like code obfuscation and watermarking which can make the source code of a program difficult to understand by humans and prove the ownership of the program. However, code obfuscation cannot avoid the source code being copied and a watermark can be defaced. A birthmark is a group of unique characteristics a program possesses that can be used to identify the program. We propose two novel dynamic birthmark systems based on the run-time heap. A dynamic birthmark is one that is extracted when the program is executing. Since it is based on the run-time behavior of the program, semantics-preserving transformations of the code like obfuscation cannot defeat dynamic birthmarks. In this regard, dynamic birthmarks are more robust compared with static birthmarks.
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\nTo the best of our knowledge, these are the first birthmark systems using heap analysis as the underlying technique. The basic idea is to take snapshots of the heap while the program is running. From the snapshots, heap graphs are constructed to model the referencing structure between objects. After going through some filtering and referencing processes, they become the birthmarks. The two birthmark systems have been devised to extract birthmarks for Java programs and JavaScript programs respectively. While the underlying ideas of the two birthmark systems are similar, the differences in nature of the two programming languages led to different implementation designs.

Inclusion-based set constraint solving is the most popular technique for whole-program points-to analysis whereby an analysis is typically formulated as repeatedly resolving constraints between points-to sets of program variables. The set union operation is central to this process. The number of points-to sets can grow as analyses become more precise and input programs become larger, resulting in more time spent performing unions and more space used storing these points-to sets. Most existing approaches focus on improving scalability of precise points-to analyses from an algorithmic perspective and there has been less research into improving the data structures behind the analyses. Bit-vectors as one of the more popular data structures have been used in several mainstream analysis frameworks to represent points-to sets. To store memory objects in bit-vectors, objects need to mapped to integral identifiers. We observe that this object-to-identifier mapping is critical for a compact points-to set representation and the set union operation. If objects in the same points-to sets (co-pointees) are not given numerically close identifiers, points-to resolution can cost significantly more space and time. Without data on the unpredictable points-to relations which would be discovered by the analysis, an ideal mapping is extremely challenging. In this paper, we present a new approach to inclusion-based analysis by compacting points-to sets through object clustering. Inspired by recent staged analysis where an auxiliary analysis produces results approximating a more precise main analysis, we formulate points-to set compaction as an optimisation problem solved by integer programming using constraints generated from the auxiliary analysis’s results in order to produce an effective mapping. We then develop a more approximate mapping, yet much more efficiently, using hierarchical clustering to compact bit-vectors. We also develop an improved representation of bit-vectors (called core bit-vectors) to fully take advantage of the newly produced mapping. Our approach requires no algorithmic change to the points-to analysis. We evaluate our object clustering on flow sensitive points-to analysis using 8 open-source programs (>3.1 million lines of LLVM instructions) and our results show that our approach can successfully improve the analysis with an up to 1.83× speed up and an up to 4.05× reduction in memory usage.

This paper presents an ongoing effort towards development of an intelligent Decision-Support System (iDSS) for fusion of information from multiple sources consisting of data from hard (physical sensors) and soft (textural sources. Primarily, this paper defines taxonomy of decision support systems for latent semantic data mining from heterogeneous data sources. A Probabilistic Latent Semantic Analysis (PLSA) approach is proposed for latent semantic concepts search from heterogeneous data sources. An architectural model for generating semantic annotation of multi-modality sensors in a modified Transducer Markup Language (TML) is described. A method for TML messages fusion is discussed for alignment and integration of spatiotemporally correlated and associated physical sensory observations. Lastly, the experimental results which exploit fusion of soft/hard sensor sources with support of iDSS are discussed.

Application of PSHA in low seismic region: A case study on NPP site in peninsular India

A evaluation for the strength of rock includes a lot of uncertainty due to existence of discontinuity surface and weakness plain in the rock mass, so essential test results and other data for the resonable strength analysis are absolutely insufficient. Therefore, a analytical technique to reduce such uncertainty can be required. A probabilistic analysis technique has mainly to make up for the uncertainty to investigate the strength of rock mass. Recently, a artificial neural networks, as a more newly analysis method to solve several problems in the existing analysis methodology, trends to apply to study on the rock strength. In this study the unconfined compressive strength from basic physical property values of sedimentary rock, black shale and red shale, distributed in Daegu metropolitan area is estimated, using the artificial neural networks. And the applicability of the analysis method is investigated. From the results, it is confirmed that the unconfined compressive strength of the sedimentary rock can be easily and efficiently predicted by the analysis technique with the artificial neural networks.

Malware is a problem spread out worldwide. Current techniques to analyze these malware are static analysis technique and dynamic analysis technique. Later, the two analysis technique is combined into a technique called hybrid analysis technique. This paper discusses on the current analysis technique and introduces a new approach towards the hybrid analysis technique by introducing memory analysis technique into it. The expected outcome of producing memory analysis technique in hybrid analysis technique will be discussed in the latter part of this paper.

In the i‐vector/probabilistic linear discriminant analysis (PLDA) technique, the PLDA backend classifier is modelled on i‐vectors. PLDA defines an i‐vector subspace that compensates the unwanted variability and helps to discriminate among speaker‐phrase pairs. The channel or session variability manifested in i‐vectors are known to be nonlinear in nature. PLDA training, however, assumes the variability to be linearly separable, thereby causing loss of important discriminating information. Besides, the i‐vector estimation, itself, is known to be poor in case of short utterances. This paper attempts to address these issues using a simple hierarchy‐based system. A modified fuzzy‐clustering technique is employed to divide the feature space into more characteristic feature subspaces using vocal source features. Thereafter, a separate i‐vector/PLDA model is trained for each of the subspaces. The sparser alignment owing to subspace‐specific universal background model and the relatively reduced dimensions of variability in individual subspaces help to train more effective i‐vector/PLDA models. Also, vocal source features are complementary to mel frequency cepstral coefficients, which are transformed into i‐vectors using mixture model technique. As a consequence, vocal source features and i‐vectors tend to have complementary information. Thus using vocal source features for classification in a hierarchy tree may help to differentiate some of the speaker‐phrase classes, which otherwise are not easily discriminable based on i‐vectors. The proposed technique has been validated on Part 1 of RSR2015 database, and it shows a relative equal error rate reduction of up to 37.41% with respect to the baseline i‐vector/PLDA system.

A probabilistic analysis technique for single event transient sensitivity evaluation of phase-lock-loops

This paper is the first of a two part study summarizing the results of a research program on the survivability analysis of command and control communication (C^{3}) systems. In the first part, we consider the formulation of realistic survivability models and criteria, determine the fundamental physical problems affecting survivability analysis, and determine suitable approaches to the survivability problem. The second part first discusses algorithms for deterministic analysis of C3systems to identify bottlenecks and more vulnerable elements in network structure. The problems encountered in probabilistic survivability analysis and basic probabilistic analysis techniques are then discussed. Finally, an efficient, practical probabilistic survivability analysis procedure is given and an example of its use is presented.

This paper investigates the design and evaluation of a personalized Recommender System (RS) using implicit social trust from Online Social Networks (OSNs), particularly to solve new users' recommendation problems. The proposed system builds implicit trust based on the interrelation between an active user and his/ her friends in the popular social microblogger Twitter, by considering aspects such as retweet actions and followers/followings lists. The measured trust values are used to vote for friends' opinions held in the posted tweets about a certain product such as movies. The higher trust parameters to a friend the more his/her opinions anticipate in recommendations encounter. Firstly, Friends' opinions are obtained by a probabilistic sentiment analysis technique to extract the opinions in form of multi-point scale of ratings from short tweets. Secondly, trust relation aspects are extracted from user's friends accounts. Further, a genetic algorithm is used to optimize social trust parameters. Thirdly, this paper considers the Support Vector Regression algorithm (SVR) to predict ratings for the active user. Our experimental results show that the proposed approach outperforms several related works in terms of accuracy using real world data from Twitter. These results can have a promising effect when solving new users, so called cold-start problem, to the systems by integrating users' OSNs.