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Board games are a great source of entertainment for all ages, as they create a competitive and engaging environment, as well as stimulating learning and strategic thinking. It is common for digital versions of board games, as any other type of digital games, to offer the option to select the difficulty of the game. This is usually done by customizing the search parameters of the AI algorithm. However, this approach cannot be extended to General Game Playing agents, as different games might require different parametrization for each difficulty level. In this paper, we present a general approach to implement an artificial intelligence opponent with difficulty levels for zero-sum games, together with a propose of a Minimax-MCTS hybrid algorithm, which combines the minimax search process with GGP aspects of MCTS. This approach was tested in our mobile application LoBoGames, an extensible board games platform, that is intended to have an broad catalog of games, with an emphasis on accessibility: the platform is friendly to visually-impaired users, and is compatible with more than 92\% of Android devices. The tests in this work indicate that both the hybrid Minimax-MCTS and the new difficulty adjustment system are promising GGP approaches that could be expanded in future work.

Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introduced to support research in the field of self-adaptive systems. While these artifacts have shown their value, it is not clear to what extent these artifacts support research on problems in self-adaptation that are relevant to industry. This paper provides a set of guidelines for artifacts that aim at supporting industry-relevant research on self-adaptation. The guidelines that are grounded on data obtained from a survey with practitioners were derived during working sessions at the 17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. Artifact providers can use the guidelines for aligning future artifacts with industry needs; they can also be used to evaluate the industrial relevance of existing artifacts. We also propose an artifact template., Comment: 7 pages

The complexity and ubiquity of modern computing systems is a fertile ground for anomalies, including security and privacy breaches. In this paper, we propose a new methodology that addresses the practical challenges to implement anomaly detection approaches. Specifically, it is challenging to define normal behavior comprehensively and to acquire data on anomalies in diverse cloud environments. To tackle those challenges, we focus on anomaly detection approaches based on system performance signatures. In particular, performance signatures have the potential of detecting zero-day attacks, as those approaches are based on detecting performance deviations and do not require detailed knowledge of attack history. The proposed methodology leverages an analytical performance model and experimentation and allows to control the rate of false positives in a principled manner. The methodology is evaluated using the TPCx-V workload, which was profiled during a set of executions using resource exhaustion anomalies that emulate the effects of anomalies affecting system performance. The proposed approach was able to successfully detect the anomalies, with a low number of false positives (precision 90%-98%)., Comment: 2020 Mediterranean Communication and Computer Networking Conference (MedComNet)

Security is one of the most relevant concerns in cloud computing. With the evolution of cyber-security threats, developing innovative techniques to thwart attacks is of utmost importance. One recent method to improve cloud computing security is Moving Target Defense (MTD). MTD makes use of dynamic reconfiguration in virtualized environments to "confuse" attackers or to nullify their knowledge about the system state. However, there is still no consolidated mechanism to evaluate the trade-offs between availability and security when using MTD on cloud computing. The evaluation through measurements is complex as one needs to deal with unexpected events as failures and attacks. To overcome this challenge, we intend to propose a set of models to evaluate the availability and security of MTD in cloud computing environments. The expected results include the quantification of availability and security levels under different conditions (e.g., different software aging rates, varying workloads, different attack intensities)., Comment: Student Forum paper of the 15th European Dependable Computing Conference (EDCC 2019)

Due to recent technological developments, Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been successfully used to process and extract knowledge from a variety of complex problems. However, a thorough ML approach is complex and highly dependent on the problem at hand. Additionally, implementing the logic required to execute the experiments is no small nor trivial deed, consequentially increasing the probability of faulty code which can compromise the results. Propheticus is a data-driven framework which results of the need for a tool that abstracts some of the inherent complexity of ML, whilst being easy to understand and use, as well as to adapt and expand to assist the user's specific needs. Propheticus systematizes and enforces various complex concepts of an ML experiment workflow, taking into account the nature of both the problem and the data. It contains functionalities to execute all the different tasks, from data preprocessing, to results analysis and comparison. Notwithstanding, it can be fairly easily adapted to different problems due to its flexible architecture, and customized as needed to address the user's needs.

Modern intrusion detection systems (IDSes) for virtualized environments are deployed in the virtualization layer with components inside the virtual machine monitor (VMM) and the trusted host virtual machine (VM). Such IDSes can monitor at the same time the network and host activities of all guest VMs running on top of a VMM being isolated from malicious users of these VMs. We refer to IDSes for virtualized environments as VMM-based IDSes. In this work, we analyze state-of-the-art intrusion detection techniques applied in virtualized environments and architectures of VMM-based IDSes. Further, we identify challenges that apply specifically to benchmarking VMM-based IDSes focussing on workloads and metrics. For example, we discuss the challenge of defining representative baseline benign workload profiles as well as the challenge of defining malicious workloads containing attacks targeted at the VMM. We also discuss the impact of on-demand resource provisioning features of virtualized environments (e.g., CPU and memory hotplugging, memory ballooning) on IDS benchmarking measures such as capacity and attack detection accuracy. Finally, we outline future research directions in the area of benchmarking VMM-based IDSes and of intrusion detection in virtualized environments in general., Comment: SPEC (Standard Performance Evaluation Corporation) Research Group --- IDS Benchmarking Working Group

Modern virtualized service infrastructures expose attack vectors that enable attacks of high severity, such as attacks targeting hypervisors. A malicious user of a guest VM (virtual machine) may execute an attack against the underlying hypervisor via hypercalls, which are software traps from a kernel of a fully or partially paravirtualized guest VM to the hypervisor. The exploitation of a vulnerability of a hypercall handler may have severe consequences such as altering hypervisor's memory, which may result in the execution of malicious code with hypervisor privilege. Despite the importance of vulnerabilities of hypercall handlers, there is not much publicly available information on them. This significantly hinders advances towards securing hypercall interfaces. In this work, we provide in-depth technical information on publicly disclosed vulnerabilities of hypercall handlers. Our vulnerability analysis is based on reverse engineering the released patches fixing the considered vulnerabilities. For each analyzed vulnerability, we provide background information essential for understanding the vulnerability, and information on the vulnerable hypercall handler and the error causing the vulnerability. We also show how the vulnerability can be triggered and discuss the state of the targeted hypervisor after the vulnerability has been triggered., Comment: SPEC (Standard Performance Evaluation Corporation) Research Group --- IDS Benchmarking Working Group

Fast Abstracts at EDCC 2012 are short presentations, aiming to serve as a rapid and flexible mechanism to report on current work that may or may not be complete, introduce new ideas to the community, and state positions on controversial issues or open problems. This way, fast abstracts provide an opportunity to introduce new work, or present radical opinions, and receive early feedback from the community. Contributions are welcome from both academia and industry. The goal of the Student Forum is to encourage students to attend EDCC 2012 and present their work, exchange ideas with researchers and practitioners, and get early feedback on their research efforts. All papers were peer-reviewed by at least three program committee members, and the authors were provided with detailed comments on their work. In the end we had one accepted paper for the Student forum., Comment: Ninth European Dependable Computing Conference - EDCC 2012, Sibiu, Romania, May 8-11, 2012