Search Results (3)

Privacy policies are critical for helping individuals make decisions on the usage of information systems. However, as a common language phenomenon, ambiguity occurs pervasively in privacy policies and largely impedes their usefulness. The existing research focuses on the identification of individual vague words or sentences, without considering the context of documents, which may cause a significant amount of false vagueness. Our goal is to automatically detect the potential false vagueness and the related supporting evidence, which illustrates or explains the vagueness, and therefore probably assist in alleviating the vagueness. We firstly analyze the public manual annotations and define four common patterns of false vagueness and three types of supporting evidence. Then we propose the approach of the F·vague-Detector to automatically detect the supporting evidence and then locate the corresponding potential false vagueness. According to our analysis, about 29–39% of individual vague sentences have at least one clarifying sentence in the documents, and experiments show good performance of our approach, with recall of 66.98–67.95%, precision of 70.59–94.85%, and $F_1$ of 69.24–78.51% on the potential false vagueness detection. Detecting the vagueness of isolated sentences without considering their context within the whole document would bring about one-third potential false vagueness, and our approach can detect this potential false vagueness and the alleviating evidence effectively. Full article

The challenging issues of computer networks and databases are not only the intrusion detection but also the reduction of false positives and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior, which are assumed to be potential intrusions or threats. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behaviors are hard to predict as there is no precise boundary differentiating one from another. Here, rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach consisting of rough set theory and intuitionistic fuzzy set theory is proposed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of both rough set and intuitionistic fuzzy set to deal with inherent uncertainty, vagueness, and indiscernibility in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. A data instance can possibly or certainly belong to a class with degrees of membership and non-membership. The empirical study with a real-world and a synthetic dataset demonstrates that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29%, respectively. Full article

This paper proposes neutrosophic vague N-soft sets which is composed of neutrosophic vague sets and N-soft sets for the first time. The new hybrid model includes a pair of asymmetric functions: truth-membership and false-membership, and an indeterminacy-membership function. Some useful operations and propositions are given and illustrated by examples. Moreover, a method of priority relation ranking based on neutrosophic vague N-soft sets is presented. The validity of the method is verified by comparison. It is more flexible and reasonable to use this method in our daily life. Finally, a potential application of multi-attribute decision making is presented. Full article