Publications

An Optimization Approach for Structured Agent-Based Provider/Receiver Tasks

Baraka K., Couto M., Melo F.S., Veloso M.

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems

2019

Conference Paper

Abstract:

This work contributes an optimization framework in the context of structured interactions between an agent playing the role of a ‘provider’ and a human ‘receiver’. Examples of provider/receiver interactions of interest include ones between occupational therapist and patient, or teacher and student. We specifically consider tasks where the provider agent needs to plan a sequence of actions with a fixed horizon, where actions are organized along a hierarchy with increasing probabilities of success and associated costs. The goal of the provider is to achieve a success with the lowest expected cost possible. In our application domains, a success may be for instance eliciting a desired behavior or a correct response from the receiver. We present a linear-time optimal planning algorithm that generates cost-optimal sequences for given action parameters. We also provide proofs for a number of properties of optimal solutions that align with typical human provider strategies. Finally, we instantiate our general formulation in the context of robot-assisted therapy tasks for children with Autism Spectrum Disorders (ASD). In this context, we present methods for determining action parameters, namely (1) an online survey with experts for determining action costs, and (2) a probabilistic model of child response based on data collected in a real child-robot interaction scenario. Our contributions may unlock increased levels of adaptivity for agents introduced in a variety of assistive contexts.

Analysis and design of electric power grids with p-robustness guarantees using a structural hybrid system approach

Ramos G., Pequito S., Aguiar A.P., Kar S.

2015 European Control Conference, ECC 2015

2015

Conference Paper

Abstract:

In this paper, we address the analysis of resilience properties related to electric power grids modeled as a (large) dynamical system. To this end, we introduce the notion of p-robustness as the capability of ensuring the proper functioning of the electric power grids, in the sense of guaranteeing generic controllability of the associated dynamical system, under arbitrary p transmission line failures. Then, we provide conditions under which the electric power grid is p-robust, and an algorithm that determines the minimum number of transmission lines in the electric power grid that is need to add in order to transform a non-robust (0-robust) electric power grid into a 1-robust electric power grid. Further, we discuss how the methodology can be extended to ensure p-robustness with a relatively small number of additional transmission lines. We present an illustrative example of the proposed analysis and methodology using the IEEE 39-bus system, whose dynamical model is described by 127 state variables.

Analysis of VM migration scheduling as moving target defense against insider attacks

Torquato M., Maciel P., Vieira M.

SAC'21

2021

Conference Paper

Abstract:

As cybersecurity threats evolve, cloud computing defenses must adapt to face new challenges. Unfortunately, due to resource sharing, cloud computing platforms open the door for insider attacks, which consist of malicious actions from cloud authorized users (e.g., clients of an Infrastructure-as-a-Service (IaaS) cloud) targeting the co-hosted users or the underlying provider environment. Virtual machine (VM) migration is a Moving Target Defense (MTD) technique to mitigate insider attacks effects, as it provides VMs positioning manageability. However, there is a clear demand for studies quantifying the security benefits of VM migration-based MTD considering different system architecture configurations. This paper tries to fill such a gap by presenting a Stochastic Reward Net model for the security evaluation of a VM migration-based MTD. The security metric of interest is the probability of attack success. We consider multiple architectures, ranging from one physical machine pool (without MTD) up to four physical machine pools. The evaluation also considers the unavailability due to VM migration. The key contributions are i) a set of results highlighting the probability of insider attacks success over time in different architectures and VM migration schedules, and ii) suggestions for selecting VMs as candidates for MTD deployment based on the tolerance levels of the attack success probability. The results are validated against simulation results to confirm the accuracy of the model.

Analysis of VM Migration Scheduling as Moving Target Defense against insider attacks

Torquato M., Maciel P., Vieira M.

ACM/SIGAPP Symposium on Applied Computing (ACM SAC 2021)

2020

Conference Paper

Abstract:

As cybersecurity threats evolve, cloud computing defenses must adapt to face new challenges. Unfortunately, due to resource sharing , cloud computing platforms open the door for insider attacks, which consist of malicious actions from cloud authorized users (e.g., clients of an Infrastructure-as-a-Service (IaaS) cloud) targeting the co-hosted users or the underlying provider environment. Virtual machine (VM) migration is a Moving Target Defense (MTD) technique to mitigate insider attacks effects, as it provides VMs positioning manageability. However, there is a clear demand for studies quantifying the security benefits of VM migration-based MTD considering different system architecture configurations. This paper tries to fill such a gap by presenting a Stochastic Reward Net model for the security evaluation of a VM migration-based MTD. The security metric of interest is the probability of attack success. We consider multiple architectures, ranging from one physical machine pool (without MTD) up to four physical machine pools. The evaluation also considers the unavailability due to VM migration. The key contributions are i) a set of results highlighting the probability of insider attacks success over time in different architectures and VM migration schedules, and ii) suggestions for selecting VMs as candidates for MTD deployment based on the tolerance levels of the attack success probability. The results are validated against simulation results to confirm the accuracy of the model. CCS CONCEPTS • Security and privacy → Distributed systems security; • Computing methodologies → Model development and analysis; • Computer systems organization → Availability;

ANSIG – An analytic signature for permutation-invariant two-dimensional shape representation

Rodrigues J.J., Aguiar P.M.Q., Xavier J.M.F.

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

2008

Conference Paper

Abstract:

Many applications require a computer representation of 2D shape, usually described by a set of 2D points. The challenge of this representation is that it must not only capture the characteristics of the shape but also be invariant to relevant transformations. Invariance to geometric transformations, such as translation, rotation and scale, has received attention in the past, usually under the assumption that the points are previously labeled, i.e., that the shape is characterized by an ordered set of landmarks. However, in many practical scenarios the landmarks are obtained from an automatic process, e.g., edge/corner detection, thus without natural ordering. In this paper, we represent 2D shapes in a way that is invariant to the permutation of the landmarks. Within our framework, a shape is mapped to an analytic function on the complex plane, leading to what we call its analytic signature (ANSIG). We show that different shapes lead to different ANSIGs but that shapes that differ by a permutation of the landmarks lead to the same ANSIG, i.e., that our representation is a maximal invariant with respect to the permutation group. To store an ANSIG, it suffices to sample it along a closed contour in the complex plane. We further show how easy it is to factor out geometric transformations when comparing shapes using the ANSIG representation. We illustrate the ANSIG capabilities in shape-based image classification.

Architecture-based run-time fault diagnosis

Casanova P., Schmerl B., Garlan D., Abreu R.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2011

Conference Paper

Abstract:

An important step in achieving robustness to run-time faults is the ability to detect and repair problems when they arise in a running system. Effective fault detection and repair could be greatly enhanced by run-time fault diagnosis and localization, since it would allow the repair mechanisms to focus adaptation effort on the parts most in need of attention. In this paper we describe an approach to run-time fault diagnosis that combines architectural models with spectrum-based reasoning for multiple fault localization. Spectrum-based reasoning is a lightweight technique that takes a form of trace abstraction and produces a list (ordered by probability) of likely fault candidates. We show how this technique can be combined with architectural models to support run-time diagnosis that can (a) scale to modern distributed software systems; (b) accommodate the use of black-box components and proprietary infrastructure for which one has neither a specification nor source code; and (c) handle inherent uncertainty about the probable cause of a problem even in the face of transient faults and faults that arise only when certain combinations of system components interact.

Architecture-based self-protection: Composing and reasoning about denial-of-service mitigations

Schmerl B., Camara J., Gennari J., Garlan D., Casanova P., Moreno G.A., Glazier T.J., Barnes J.M.

ACM International Conference Proceeding Series

2014

Conference Paper

Abstract:

Security features are often hardwired into software applications, making it difficult to adapt security responses to reflect changes in runtime context and new attacks. In prior work, we proposed the idea of architecture-based self-protection as a way of separating adaptation logic from application logic and providing a global perspective for reasoning about security adaptations in the context of other business goals. In this paper, we present an approach, based on this idea, for combating denial-of-service (DoS) attacks. Our approach allows DoS-related tactics to be composed into more sophisticated mitigation strategies that encapsulate possible responses to a security problem. Then, utility-based reasoning can be used to consider different business contexts and qualities. We describe how this approach forms the underpinnings of a scientific approach to self-protection, allowing us to reason about how to make the best choice of mitigation at runtime. Moreover, we also show how formal analysis can be used to determine whether the mitigations cover the range of conditions the system is likely to encounter, and the effect of mitigations on other quality attributes of the system. We evaluate the approach using the Rainbow self-adaptive framework and show how Rainbow chooses DoS mitigation tactics that are sensitive to different business contexts.

Arrhythmia detection and classification using morphological and dynamic features of ECG signals

Ye C., Coimbra M.T., Vijaya Kumar B.V.K.

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

2010

Conference Paper

Abstract:

Computer-assisted cardiac arrhythmia detection and classification can play a significant role in the management of cardiac disorders. In this paper, we propose a new approach for arrhythmia classification based on a combination of morphological and dynamic features. Wavelet Transform (WT) and Independent Component Analysis (ICA) are applied separately to each heartbeat to extract corresponding coefficients, which are categorized as `morphological’ features. In addition, RR interval information is also obtained characterizing the `rhythm’ around the corresponding heartbeat providing `dynamic’ features. These two different types of features are then concatenated and Support Vector Machine (SVM) is utilized for the classification of heartbeats into 15 classes. The procedure is applied to the data from two ECG leads independently and the two results are fused for the final decision. Compare the two classification results and the classification result is kept if the two are identical or the one with greater classification confidence is picked up if the two are inconsistent. The proposed method was tested over the entire MIT-BIH Arrhythmias Database and it yields an overall accuracy of 99.66% on 85945 heartbeats, better than any other published results.

ASR-based exercises for listening comprehension practice in European Portuguese

Pellegrini T., Correia R., Trancoso I., Baptista J., Mamede N., Eskenazi M.

Computer Speech and Language

2013

Article

Abstract:

Spoken European Portuguese (EP) is known to be difficult to understand for L2 learners, due to phenomena such as strong vowel reduction. In this paper, we present a method to automatically generate exercises aimed at improving listening comprehension skills in EP. Learners identify the words pronounced in real speech utterances. The exercises introduce two innovative aspects: using broadcast news videos for curriculum and automatically generating exercises with material updated on a daily basis. The videos are automatically transcribed by a speech recognition engine. A filtering chain, used to select appropriate sentences, was validated by a first survey comprised of both manually and automatically selected sentences. Both sets were assigned good to very good subjective quality scores. A second survey concerned the features of the exercise interface. Subjects with varying self-reported exposure to Portuguese as a second language tested several interfaces and functionalities and highlighted their preferred features. The results confirmed that the largest difficulty was the fast speech rate. All participants valued slowed-down audio and video documents, though this feature was more often used by the lowest proficiency subjects. The exercises were integrated into a Web platform where they are automatically updated daily. Though further evaluation is needed to find whether the platform affords skill acquisition, it is expected to be particularly valuable for distance learners who need opportunities to access authentic audio documents in EP.