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Publicações

2020

Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review

Autores
Martins, N; Cruz, JM; Cruz, T; Abreu, PH;

Publicação
IEEE ACCESS

Abstract
Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learning poses as a potential solution. Adversarial machine learning is a research area that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively studied specifically in the area of image recognition, where minor modifications are performed on images that cause a classifier to produce incorrect predictions. However, in other fields, such as intrusion and malware detection, the exploration of such methods is still growing. The aim of this survey is to explore works that apply adversarial machine learning concepts to intrusion and malware detection scenarios. We concluded that a wide variety of attacks were tested and proven effective in malware and intrusion detection, although their practicality was not tested in intrusion scenarios. Adversarial defenses were substantially less explored, although their effectiveness was also proven at resisting adversarial attacks. We also concluded that, contrarily to malware scenarios, the variety of datasets in intrusion scenarios is still very small, with the most used dataset being greatly outdated.

2020

JAY: Adaptive Computation Offloading for Hybrid Cloud Environments

Autores
Silva, J; Marques, ERB; Lopes, LMB; Silva, F;

Publicação
2020 FIFTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC)

Abstract
Edge computing is a hot research topic given the ever-increasing requirements of mobile applications in terms of computation and communication and the emerging Internet-of-Things with billions of devices. While ubiquitous and with considerable computational resources, devices at the edge may not be able to handle processing tasks on their own and thus resort to offloading to cloudlets, when available, or traditional cloud infrastructures. In this paper, we present JAY, a modular and extensible platform for mobile devices, cloudlets, and clouds that can manage computational tasks spawned by devices and make informed decisions about offloading to neighboring devices, cloudlets, or traditional clouds. JAY is parametric on the scheduling strategy and metrics used to make offloading decisions, providing a useful tool to study the impact of distinct offloading strategies. We illustrate the use of JAY with an evaluation of several offloading strategies in distinct cloud configurations using a real-world machine learning application, firing tasks can be dynamically executed on or offloaded to Android devices, cloudlet servers, or Google Cloud servers. The results obtained show that edge-clouds form competent computing platforms on their own and that they can effectively be meshed with cloudlets and traditional clouds when more demanding processing tasks are considered. In particular, edge computing is competitive with infrastructure clouds in scenarios where data is generated at the edge, high bandwidth is required, and a pool of computationally competent devices or an edge-server is available. The results also highlight JAY's ability of exposing the performance compromises in applications when they are deployed over distinct hybrid cloud configurations using distinct offloading strategies.

2020

On the Construction of Multi-valued Concurrent Dynamic Logics

Autores
Gomes, L;

Publicação
DYNAMIC LOGIC: NEW TRENDS AND APPLICATIONS, DALI 2019

Abstract
Dynamic logic is a powerful framework for reasoning about imperative programs. An extension with a concurrent operator, called concurrent propositional dynamic logic (CPDL) [20], was introduced to formalise programs running in parallel. In a different direction, other authors proposed a systematic method for generating multi-valued propositional dynamic logics to reason about weighted programs [15]. This paper presents the first step of combining these two frameworks to introduce uncertainty in concurrent computations. In the proposed framework, a weight is assigned to each branch of the parallel execution, resulting in a (possible) asymmetric parallelism, inherent to the fuzzy programming paradigm [2,23]. By adopting such an approach, a family of logics is obtained, called multi-valued concurrent propositional dynamic logics (GCDL(A)), parametric on an action lattice A specifying a notion of "weight" assigned to program execution. Additionally, the validity of some axioms of CPDL is discussed in the new family of generated logics.

2020

Vulnerability of Largest Normalized Residual Test and <(b)over cap> - Test to Gross Errors

Autores
Massignan, JAD; London, JBA; Vieira, CS; Miranda, V;

Publicação
2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)

Abstract
Power systems rely on a broad set of information and sensors to maintain reliable and secure operation. Proper processing of such information, to guarantee the integrity of power system data, is a requirement in any modern control centre, typically performed by state estimation associated with bad data processing algorithms. This paper shows that contrarily to a commonly assumed claim regarding bad data processing, in some cases of single gross error (GE) the noncritical measurement contaminated with GE does not present the largest normalized residual. Based on the analysis of the elements of the residual sensitivity matrix, the paper formally demonstrates that such claim does not always hold. Besides this demonstration, possible vulnerabilities for traditional bad data processing are mapped through the Undetectability Index (UI). Computational simulations carried out on IEEE 14 and IEEE 118 test systems provide insight into the paper proposition.

2020

Learning in Virtual Reality: Investigating the Effects of Immersive Tendencies and Sense of Presence

Autores
Krassmann, AL; Melo, M; Peixoto, B; Pinto, D; Bessa, M; Bercht, M;

Publicação
Virtual, Augmented and Mixed Reality. Industrial and Everyday Life Applications - 12th International Conference, VAMR 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19-24, 2020, Proceedings, Part II

Abstract
The goal of this study is to examine the effects of the sense of presence and immersive tendencies on learning outcomes while comparing different media formats (Interactive VR, Non-interactive VR and Video). An experiment was conducted with 36 students that watched a Biology lesson about the human cells. Contrary to expected, the results demonstrate that the Non-interactive VR was the most successful format. Sense of presence and immersive tendencies did not have an effect on learning gain, and the latter was not a critical factor to experience the sense of presence. The findings provide empirical evidence to help understand the influence of these variables on learning in VR. © 2020, Springer Nature Switzerland AG.

2020

Efficient procedures for the weighted squared tardiness permutation flowshop scheduling problem

Autores
Costa, MRC; Valente, JMS; Schaller, JE;

Publicação
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL

Abstract
This paper addresses a permutation flowshop scheduling problem, with the objective of minimizing total weighted squared tardiness. The focus is on providing efficient procedures that can quickly solve medium or even large instances. Within this context, we first present multiple dispatching heuristics. These include general rules suited to various due date-related environments, heuristics developed for the problem with a linear objective function, and procedures that are suitably adapted to take the squared objective into account. Then, we describe several improvement procedures, which use one or more of three techniques. These procedures are used to improve the solution obtained by the best dispatching rule. Computational results show that the quadratic rules greatly outperform the linear counterparts, and that one of the quadratic rules is the overall best performing dispatching heuristic. The computational tests also show that all procedures significantly improve upon the initial solution. The non-dominated procedures, when considering both solution quality and runtime, are identified. The best dispatching rule, and two of the non-dominated improvement procedures, are quite efficient, and can be applied to even very large-sized problems. The remaining non-dominated improvement method can provide somewhat higher quality solutions, but it may need excessive time for extremely large instances.

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