Learning-based Analysis on the Exploitability of Security Vulnerabilities

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing the accuracy of each model, dynamically training the models on a rolling window of training data, and filtering the training data by various features. Of the chosen models, the deep neural network and the support vector machine show the highest accuracy (approximately 94% and 93%, respectively), and could be developed by future researchers into an effective tool for vulnerability analysis

Machine learning to analyze single-case data : a proof of concept

Visual analysis is the most commonly used method for interpreting data from singlecase designs, but levels of interrater agreement remain a concern. Although structured aids to visual analysis such as the dual-criteria (DC) method may increase interrater agreement, the accuracy of the analyses may still benefit from improvements. Thus, the purpose of our study was to (a) examine correspondence between visual analysis and models derived from different machine learning algorithms, and (b) compare the accuracy, Type I error rate and power of each of our models with those produced by the DC method. We trained our models on a previously published dataset and then conducted analyses on both nonsimulated and simulated graphs. All our models derived from machine learning algorithms matched the interpretation of the visual analysts more frequently than the DC method. Furthermore, the machine learning algorithms outperformed the DC method on accuracy, Type I error rate, and power. Our results support the somewhat unorthodox proposition that behavior analysts may use machine learning algorithms to supplement their visual analysis of single-case data, but more research is needed to examine the potential benefits and drawbacks of such an approach

Spatial Logics for Bigraphs

Bigraphs are emerging as an interesting model for concurrent calculi, like CCS, pi-calculus, and Petri nets. Bigraphs are built orthogonally on two structures: a hierarchical place graph for locations and a link (hyper-)graph for connections. With the aim of describing bigraphical structures, we introduce a general framework for logics whose terms represent arrows in monoidal categories. We then instantiate the framework to bigraphical structures and obtain a logic that is a natural composition of a place graph logic and a link graph logic. We explore the concepts of separation and sharing in these logics and we prove that they generalise some known spatial logics for trees, graphs and tree contexts

Graph-Based Shape Analysis Beyond Context-Freeness

We develop a shape analysis for reasoning about relational properties of data structures. Both the concrete and the abstract domain are represented by hypergraphs. The analysis is parameterized by user-supplied indexed graph grammars to guide concretization and abstraction. This novel extension of context-free graph grammars is powerful enough to model complex data structures such as balanced binary trees with parent pointers, while preserving most desirable properties of context-free graph grammars. One strength of our analysis is that no artifacts apart from grammars are required from the user; it thus offers a high degree of automation. We implemented our analysis and successfully applied it to various programs manipulating AVL trees, (doubly-linked) lists, and combinations of both

Iteration Algebras for UnQL Graphs and Completeness for Bisimulation

This paper shows an application of Bloom and Esik's iteration algebras to model graph data in a graph database query language. About twenty years ago, Buneman et al. developed a graph database query language UnQL on the top of a functional meta-language UnCAL for describing and manipulating graphs. Recently, the functional programming community has shown renewed interest in UnCAL, because it provides an efficient graph transformation language which is useful for various applications, such as bidirectional computation. However, no mathematical semantics of UnQL/UnCAL graphs has been developed. In this paper, we give an equational axiomatisation and algebraic semantics of UnCAL graphs. The main result of this paper is to prove that completeness of our equational axioms for UnCAL for the original bisimulation of UnCAL graphs via iteration algebras. Another benefit of algebraic semantics is a clean characterisation of structural recursion on graphs using free iteration algebra.Comment: In Proceedings FICS 2015, arXiv:1509.0282

A Comparison Between Propensity Score Matching, Weighting, and Stratification in Multiple Treatment Groups: A Simulation Study

The application of propensity score techniques (matching, stratification, and weighting) with multiple treatment levels are similar to those used in binary groups. However, given that the application of propensity scores in multiple treatment groups is new, factors affecting the performance of matching, stratification, and weighting in multiple treatment groups are less explored. Therefore, this study was conducted to determine the performance of different propensity score techniques with multiple treatment groups under various circumstances. Specifically, the study focused on examining how the three propensity score corrective techniques perform in estimating treatment effects under (1) overt and (2) hidden types of selection bias. In this study, the performance of propensity score matching, stratification, and weighting techniques were tested under three different sample sizes and three levels of overt and hidden bias. A Monte Carlo simulation was used to generate data with specific sample sizes and levels of overt and hidden bias. A total of 54 data conditions with 1000 replications for each condition was generated to compute the average treatment effect (ATE). The difference between the pre-specified ATE and estimated ATE was calculated to evaluate the performance of propensity score techniques. Two 3x3x3x2 analyses of variance were conducted to assess the effect of propensity score technique, level of bias, sample size, and type of treatment effect on the amount of bias in estimating the treatment effect under overt and hidden bias conditions. The results provided four key findings of information about the application of propensity score analysis in multiple treatment groups. The first key finding is that the treatment effect estimate will be underestimated after imposing propensity score adjustments. Second, the treatment effect estimates are affected by the level of overt bias. Third, propensity score analysis does not account for hidden bias. The fourth finding is that the propensity score techniques performed differently in a small sample size condition. Overall, these four key findings provide cautionary notes to the users of propensity score analysis in multiple treatment groups. The study is concluded with the limitations of this study and the recommendations for future research. Keywords: Propensity score, multiple treatmen