Community discovery by propagating local and global information based on the MapReduce model

Automated, high throughput exploration of process–structure–property relationships using the MapReduce paradigm

Over the last few years, data are generated in large volume at a faster rate and there has been a remarkable growth in the need for large scale data processing systems. As data grows larger in size, data quality is compromised. Functional dependencies representing semantic constraints in data are important for data quality assessment. Executing functional dependency discovery algorithms on a single computer is hard and laborious with large data sets. MapReduce provides an enabling technology for large scale data processing. The open-source Hadoop implementation of MapReduce has provided researchers a powerful tool for tackling large-data problems in a distributed manner. The objective of this study is to extract functional dependencies between attributes from large datasets using MapReduce programming model. Attribute entropy is used to measure the inter attribute correlations, and exploited to discover functional dependencies hidden in the data.

Extracting Functional Dependencies in Large Datasets Using MapReduce Model

Social networks clustering is an NP-hard problem because it is difficult to find the communities in a reasonable time; therefore, the solutions are based on heuristics. Social networks clustering aims to collect people with common interest in one group. Several approaches have been developed for clustering social networks. In this paper the researchers, introduce a new approach to cluster social networks based on Artificial Bee Colony optimization algorithm, which is a swarm based meta-heuristic algorithm. This approach aims to maximize the modularity, which is a measure that represents the quality of network partitioning. The researchers cluster some real known social networks with the proposed algorithm and compare it with the other approaches. Their algorithm increases the modularity and gives higher quality solutions than the previous approaches.

An Artificial Bee Colony ABC Algorithm for Efficient Partitioning of Social Networks

Software obfuscation is a crucial technology to protect intellectual property and manage digital rights within our society. Despite its huge practical importance, both commercial and academic state-of-the-art obfuscation methods are vulnera-ble to a plethora of automated deobfuscation attacks, such as symbolic execution, taint analysis, or program synthesis. While several enhanced obfuscation techniques were recently proposed to thwart taint analysis or symbolic execution, they either impose a prohibitive runtime overhead or can be removed in an automated way (e. g., via compiler optimizations). In general, these techniques suffer from focusing on a single attack vector, allowing an attacker to switch to other, more effective techniques, such as program synthesis. In this work, we present L OKI , an approach for software obfuscation that is resilient against all known automated deobfuscation attacks. To this end, we use and efficiently combine multiple techniques, including a generic approach to synthesize formally verified expressions of arbitrary complexity. Contrary to state-of-the-art approaches that rely on a few hardcoded generation rules, our expressions are more diverse and harder to pattern match against. We show that even the state-of-the-art approach on Mixed-Boolean Arithmetic (MBA) deobfuscation fails to simplify them. Moreover, L OKI protects against previously unaccounted attack vectors such as program synthesis, for which it reduces the success rate to merely 19%. In a comprehensive evaluation, we show that our design incurs significantly less overhead while providing a much stronger protection level compared to existing works.

Loki: Hardening Code Obfuscation Against Automated Attacks

Social network partitioning has become a very important function. One objective for partitioning is to identify interested communities to target for marketing and advertising activities. The bottleneck to detection of these communities is the large scalability of the social network. Previous methods did not effectively address the problem because they considered the overall network. Social networks have strong locality, so designing a local algorithm to find an interested community to address this objective is necessary. In this paper, we develop a local partition algorithm, named, Personalized PageRank Partitioning, to identify the community. We compute the conductance of the social network with a Personalized PageRank and Markov chain stationary distribution of the social network, and then sweep the conductance to find the smallest cut. The efficiency of the cut can reach. In order to detect a larger scale social network, we design and implement the algorithm on a MapReduce-programming framework. Finally, we execute our experiment on several actual social network data sets and compare our method to others. The experimental results show that our algorithm is feasible and very effective.

Detecting Local Communities within a Large Scale Social Network Using Mapreduce

Satisfiability Modulo Theories (SMT) solvers have been widely applied in automated software analysis to reason about the queries that encode the essence of program semantics, relieving the heavy burden of manual analysis. Many SMT solving techniques rely on solving Boolean satisfiability problem (SAT), which is an NP-complete problem, so they use heuristic search strategies to seek possible solutions, especially when no known theorem can efficiently reduce the problem. An emerging challenge, named Mixed-Bitwise-Arithmetic (MBA) obfuscation, impedes SMT solving by constructing identity equations with both bitwise operations (and, or, negate) and arithmetic computation (add, minus, multiply). Common math theorems for bitwise or arithmetic computation are inapplicable to simplifying MBA equations, leading to performance bottlenecks in SMT solving. In this paper, we first scrutinize solvers' performance on solving different categories of MBA expressions: linear, polynomial, and non-polynomial. We observe that solvers can handle simple linear MBA expressions, but facing a severe performance slowdown when solving complex linear and non-linear MBA expressions. The root cause is that complex MBA expressions break the reduction laws for pure arithmetic or bitwise computation. To boost solvers' performance, we propose a semantic-preserving transformation to reduce the mixing degree of bitwise and arithmetic operations. We first calculate a signature vector based on the truth table extracted from an MBA expression, which captures the complete MBA semantics. Next, we generate a simpler MBA expression from the signature vector. Our large-scale evaluation on 3000 complex MBA equations shows that our technique significantly boost modern SMT solvers' performance on solving MBA formulas.

Boosting SMT solver performance on mixed-bitwise-arithmetic expressions

Mixed Boolean-Arithmetic (MBA) expression mixes bitwise operations (e.g., AND, OR, and NOT) and arithmetic operations (e.g., ADD and IMUL). It enables a semantic-preserving program transformation to convert a simple expression to a difficult-to-understand but equivalent form. MBA expression has been widely adopted as a highly effective and low-cost obfuscation scheme. However, state-of-the-art deobfuscation research proposes substantial challenges to the MBA obfuscation technique. Attacking methods such as bit-blasting, pattern matching, program synthesis, deep learning, and mathematical transformation can successfully simplify specific categories of MBA expressions. Existing MBA obfuscation must be enhanced to overcome these emerging challenges.

Software Obfuscation with Non-Linear Mixed Boolean-Arithmetic Expressions.

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MBA-Blast: Unveiling and Simplifying Mixed Boolean-Arithmetic Obfuscation

In this paper, we propose a fast MPI algorithm for Monte Carlo approximation PageRank vector of all the nodes in a graph, named Fast Fibonacci Series-Based Personal PageRank In the latter paper we will call it FFSB algorithm for short The basic ideal is very efficiently computing single random walks of a given length starting at each node in a graph More precisely, we design FFSB, which given a graph G and a length λ, outputs a single random walk of length λ at each node in G We will exhibit that the number of MPI iterations and machine time is better than the most efficient algorithm at present with machine time log2 λ (λ is the given walk length) The algorithm with the complexity 072022 × log2 λ × (g + max {L + 2 × o, 2 × g}) is optimal among a broad family of algorithms for the problem Also the empirical evaluation on real-life graph data crawled from Sina micro blog demonstrates that our algorithm is significantly more efficient than all the existing candidates in production parallel programing environment MPI

Jing Li

Papers

Detecting Local Communities within a Large Scale Social Network Using Mapreduce

Boosting SMT solver performance on mixed-bitwise-arithmetic expressions

Software Obfuscation with Non-Linear Mixed Boolean-Arithmetic Expressions.

MBA-Blast: Unveiling and Simplifying Mixed Boolean-Arithmetic Obfuscation

FFSB: Fast Fibonacci Series-Based personalized PageRank on MPI