Application of fuzzy logic to assess the quality of BPMN models

© Springer International Publishing AG, part of Springer Nature 2018. Modeling is the first stage in a Business Process’s (BP) lifecycle. A high-quality BP model is vital to the successful implementation, execution, and monitoring stages. Different works have evaluated BP models from a quality perspective. These works either used formal verification or a set of quality metrics. This paper adopts quality metric and targets models represented in Business Process Modeling and Notation (BPMN). It proposes an approach based on fuzzy logic along with a tool system developed under eclipse framework. The preliminary experimental evaluation of the proposed system shows encouraging results

Automatic rule extraction from access rules using Genetic Programming

International audienceThe security policy rules in companies are generally proposed by the Chief Security Officer (CSO), who must, for instance, select by hand which access events are allowed and which ones should be forbidden. In this work we propose a way to automatically obtain rules that gen-eralise these single-event based rules using Genetic Programming (GP), which, besides, should be able to present them in an understandable way. Our GP-based system obtains good dataset coverage and small ratios of false positives and negatives in the simulation results over real data, after testing different fitness functions and configurations in the way of coding the individuals

Identifying Devices of the Internet of Things Using Machine Learning on Clock Characteristics

The number of devices of the so-called Internet of Things (IoT) is heavily increasing. One of the main challenges for operators of large networks is to autonomously and automatically identify any IoT device within the network for the sake of computer security and, subsequently, being able to better protect and secure those. In this paper, we propose a novel approach to identify IoT devices based on the unchangeable IoT hardware setup through device specific clock behavior. One feature we use is the unavoidable fact that clocks experience “clock skew”, which results in running faster or slower than an exact clock. Clock skew along with twelve other clock related features are suitable for our approach, because we can measure these features remotely through TCP timestamps which many devices can add to their packets. We show that we are able to distinguish device models by Machine Learning only using these clock characteristics. We ensure that measurements of our approach do not stress a device or causes fault states at any time. We evaluated our approach in a large-scale real-world installation at the European Organization for Nuclear Research (CERN) and show that the above-mentioned methods let us identify IoT device models within the network

Learning Classifiers for Multimodal Image Detection

Learning Classifiers are essential analysis tools in today's era due to the rapid expansion in data-intensive applications. Every type of the classifier has own distinct architectural properties which allow to apply them in various application-specific requirements. Here, experimental work is performed to analyse the performance and accuracy of most commonly used learning classifiers applied within the scope of multimodal image classification problem space. The purpose of this study is to investigate the usefulness of the learning-classifiers with the multimodal datasets which are in many cases have the curse of dimensionality and may contain a very high volume of noise. Validation is conducted over multimodal datasets with the proposed classification algorithms operating in a parallel manner. Finally, results are discussed to explore possible ways to apply every learning classifier in multi-modal image classification applications

Epistemic Uncertainty Sampling

International audienc