Research Outputs
Optimal multiculture network design for maximizing resilience in the face of multiple correlated failures
Current data networks are highly homogeneous because of management, economic, and interoperability reasons. This technological homogeneity introduces shared risks, where correlated failures may entirely disrupt the network operation and impair multiple nodes. In this paper, we tackle the problem of improving the resilience of homogeneous networks, which are affected by correlated node failures, through optimal multiculture network design. Correlated failures regarded here are modeled by SRNG events. We propose three sequential optimization problems for maximizing the network resilience by selecting as different node technologies, which do not share risks, and placing such nodes in a given topology. Results show that in the 75% of real-world network topologies analyzed here, our optimal multiculture design yields networks whose probability that a pair of nodes, chosen at random, are connected is 1, i.e., its ATTR metric is 1. To do so, our method efficiently trades off the network heterogeneity, the number of nodes per technology, and their clustered location in the network. In the remaining 25% of the topologies, whose average node degree was less than 2, such probability was at least 0.7867. This means that both multiculture design and topology connectivity are necessary to achieve network resilience.
A Spatial-Spectral classification method based on deep learning for controlling pelagic fish landings in Chile
Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red–Green–Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.
Resilient multiculture network design in the presence of exploit-triggered correlated failures
Data networks are typically equipped with the same hardware and software stacks. Correlated attacks exploiting shared vulnerabilities at the nodes may result in massive failures that disrupt network operation. In this paper, multiple correlated failures that may negatively impact a monoculture network are analyzed and a methodology to reduce their effects is proposed. The proposed methodology consists of introducing diversity into the network components by optimally selecting both the number of different network nodes and their locations within the network. First, an algorithm is proposed to introduce node diversity in the topology considering nodes' vulnerability indexes, which are associated with node vendors. Next, two different optimal node placement algorithms are proposed. The first algorithm aims to cluster nodes of the same type to maintain network connectivity, while the second seeks to maximize the network centrality metric to identify key nodes in the network. Our results show that reliability can increase up to 50% when compared to a monoculture design.