UML-F: A Modeling Language for Object-Oriented Frameworks

The paper presents the essential features of a new member of the UML language family that supports working with object-oriented frameworks. This UML extension, called UML-F, allows the explicit representation of framework variation points. The paper discusses some of the relevant aspects of UML-F, which is based on standard UML extension mechanisms. A case study shows how it can be used to assist framework development. A discussion of additional tools for automating framework implementation and instantiation rounds out the paper.Comment: 22 pages, 10 figure

Domain Adaptation for Statistical Classifiers

The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain

Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain. © 2019, The Author(s)

Scalable Multiagent Coordination with Distributed Online Open Loop Planning

We propose distributed online open loop planning (DOOLP), a general framework for online multiagent coordination and decision making under uncertainty. DOOLP is based on online heuristic search in the space defined by a generative model of the domain dynamics, which is exploited by agents to simulate and evaluate the consequences of their potential choices. We also propose distributed online Thompson sampling (DOTS) as an effective instantiation of the DOOLP framework. DOTS models sequences of agent choices by concatenating a number of multiarmed bandits for each agent and uses Thompson sampling for dealing with action value uncertainty. The Bayesian approach underlying Thompson sampling allows to effectively model and estimate uncertainty about (a) own action values and (b) other agents' behavior. This approach yields a principled and statistically sound solution to the exploration-exploitation dilemma when exploring large search spaces with limited resources. We implemented DOTS in a smart factory case study with positive empirical results. We observed effective, robust and scalable planning and coordination capabilities even when only searching a fraction of the potential search space

A Virtual Machine Introspection Based Multi-Service, Multi-Architecture, High-Interaction Honeypot for IOT Devices

Internet of Things (IoT) devices are quickly growing in adoption. The use case for IoT devices runs the gamut from household applications (such as toasters, lighting, and thermostats) to medical, battlefield, or Industrial Control System (ICS) applications used in life or death situations. A disturbing trend is that for IoT devices is that they are not developed with security in mind. This lack of security has led to the creation of massive botnets that conduct nefarious acts. A clear understanding of the threat landscape IoT devices face is needed to address these security issues. One technique used to understand threats is to deploy honeypots that masquerade as legitimate IoT devices and analyze what attackers do to them. Current research shows that it is challenging to create high-interaction IoT honeypots due to the heterogeneous nature of IoT devices and the lack of emulators. This study seeks to answer the research question, How can an ideal IoT honeypot emulate existing IoT devices and be high-interaction by allowing the inspection of the full OS running on the device to detect when an attack is occurring, support an arbitrary number of services, and record metrics related to the attack. The answer to this question would allow for the development of an IoT honeypot that provides valuable insight into how threat actors attack, exploit, and use IoT devices to their advantage. This research used design science research methods to explore the creation of a Virtual Machine Introspection-based high-interaction honeypot framework for IoT devices that is capable of emulating existing devices, gathering Operating-System-level artifacts, and monitoring an arbitrary number of services. Two artifact were developed: a theoretical framework and an instantiation of the theoretical framework. The theoretical framework drove the design of the framework instantiation, while the instantiation validated the theoretical framework design. The framework design goals were validated using two case studies that emulated consumer-grade IoT devices and infected them with the Reaper and Silex botnets