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Advanced cyber attackers can penetrate enterprise networks and steal critical documents containing intellectual property despite all access control measures. Cyber deception is one of many solutions to protect critical documents after an attacker penetrates the network. It requires the generation and deployment of decoys such as fake text. The comprehensibility of a fake text document can affect the required time and effort for an attack to succeed. However, existing cybersecurity research has given limited attention to exploring the comprehensibility features of text for fake document generation. This article presents a novel method to generate believable fake text documents by measuring and manipulating the comprehensibility of legit text within a genetic algorithm (GA) framework. For measuring text comprehensibility, we adopt a set of quantitative measures based on qualitative principles of psycholinguistics and reading comprehension: connectivity, dispersion, and sequentiality. Our user-study analysis indicates that the quantitative comprehensibility measures can approximate the degree of human effort required to comprehend a fake text document in contrast to a legit text. For manipulating text comprehensibility, we develop a multiobjective, multimutation GA that modifies a legit document to Pareto-optimally alter its comprehensibility measures and generate hard-to-comprehend, believable fake documents. Our experiments show that the proposed algorithm successfully generates fake documents for a broader class of legit documents with varied text characteristics when compared to baselines from previous research. Hence, the application of our method can help improve cyber deception systems by providing more believable yet hard-to-comprehend fake documents to mislead cyber attackers. [ABSTRACT FROM AUTHOR]

Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially the Naive Bayes classifier is often applied in this regard, citing predictive performance and comprehensibility as its major strengths, a number of alternative Bayesian algorithms that boost the possibility of constructing simpler networks with fewer nodes and arcs remain unexplored. This study contributes to the literature by considering 15 different Bayesian Network (BN) classifiers and comparing them to other popular machine learning techniques. Furthermore, the applicability of the Markov blanket principle for feature selection, which is a natural extension to BN theory, is investigated. The results, both in terms of the AUC and the recently introduced H-measure, are rigorously tested using the statistical framework of Demšar. It is concluded that simple and comprehensible networks with less nodes can be constructed using BN classifiers other than the Naive Bayes classifier. Furthermore, it is found that the aspects of comprehensibility and predictive performance need to be balanced out, and also the development context is an item which should be taken into account during model selection. [ABSTRACT FROM AUTHOR]

Ant colony optimization (ACO) can be applied to the data mining field to extract rule-based classifiers. The aim of this paper is twofold. On the one hand, we provide an overview of previous ant-based approaches to the classification task and compare them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study. On the other hand, a new ant-based classification technique is proposed, named AntMiner+. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Furthermore, the commonly encountered problem in ACO of setting system parameters is dealt with in an automated, dynamic manner. Our benchmarking experiments show an AntMiner+ accuracy that is superior to that obtained by the other AntMiner versions, and competitive or better than the results achieved by the compared classification techniques. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) have shown strong generalization ability in a number of application areas, including protein structure prediction. However, the poor comprehensibility hinders the success of the SVM for protein structure prediction. The explanation of how a decision made is important for accepting the machine learning technology, especially for applications such as bioinformatics. The reasonable interpretation is not only useful to guide the "wet experiments," but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. On the other hand, a decision tree has good comprehensibility. In this paper, a novel approach to rule generation for protein secondary structure prediction by integrating merits of both the SVM and decision tree is presented. This approach combines the SVM with decision tree into a new algorithm called SVM_DT, which proceeds in three steps. This algorithm first trains an SVM. Then, a new training set is generated through careful selection from the output of the SVM. Finally, the obtained training set is used to train a decision tree learning system and to extract the corresponding rule sets. The results of the experiments of protein secondary structure prediction on RS126 data set show that the comprehensibility of SVM_DT is much better than that of the SVM. Moreover, the generalization ability of SVM_DT is better than that of C4.5 decision trees and is similar to that of the SVM. Hence, SVM_DT can be used not only for prediction, but also for guiding biological experiments [ABSTRACT FROM PUBLISHER]

This paper describes a study on the impact of software structure on maintainability aspects such as comprehensibility, locality, modifiability, and reusability in a distributed system environment. The study was part of a project at the University of Kaiserslautern, West Germany, to design and implement LADY, a LAnguage for Distributed sYstems. The study addressed the impact of software structure from two perspectives. The language designer's perspective was to evaluate the general impact of the set of structural concepts chosen for LADY on the maintainability of software systems implemented in LADY. The language user's perspective was to derive structural criteria (metrics), measurable from LADY systems, that allow the explanation or prediction of the software maintenance behavior. A controlled maintenance experiment was conducted involving twelve medium-size distributed software systems; six of these systems were implemented in LADY, the other six systems in an extended version of sequential Pascal. The benefits of the structural LADY concepts were judged based on a comparison of the average maintenance behavior of the LADY systems and the Pascal systems; the maintenance metrics were derived by analyzing the interdependence between structure and maintenance behavior of each individual LADY system. [ABSTRACT FROM AUTHOR]