Intrusion Detection Systems in SDN-based Self-Healing PMU Networks

Nowadays, Power grids are critical infrastructures on which everything else relies, and their correct behavior is of the highest priority. New smart devices are being deployed to be able to manage and control power grids more efficiently and avoid instability. However, the deployment of such smart devices like Phasor Measurement Units (PMU) and Phasor Data Concentrators (PDC), open new opportunities for cyber attackers to exploit network vulnerabilities. If a PDC is compromised, all data coming from PMUs to that PDC is lost, reducing network observability. Our approach to solve this problem is to develop an Intrusion detection System (IDS) in a Software-defined network (SDN). allowing the IDS system to detect compromised devices and use that information as an input for a self-healing SDN controller, which redirects the data of the PMUs to a new, uncompromised PDC, maintaining the maximum possible network observability at every moment. During this research, we have successfully implemented Self-healing in an example network with an SDN controller based on Ryu controller. We have also assessed intrinsic vulnerabilities of Wide Area Management Systems (WAMS) and SCADA networks, and developed some rules for the Intrusion Detection system which specifically protect vulnerabilities of these networks. The integration of the IDS and the SDN controller was also successful. \\To achieve this goal, the first steps will be to implement an existing Self-healing SDN controller and assess intrinsic vulnerabilities of Wide Area Measurement Systems (WAMS) and SCADA networks. After that, we will integrate the Ryu controller with Snort, and create the Snort rules that are specific for SCADA or WAMS systems and protocols.

Clinical factors associated with a Candida albicans Germ Tube Antibody positive test in Intensive Care Unit patients

Background: Poor outcomes of invasive candidiasis (IC) are associated with the difficulty in establishing the microbiological diagnosis at an early stage. New scores and laboratory tests have been developed in order to make an early therapeutic intervention in an attempt to reduce the high mortality associated with invasive fungal infections. Candida albicans IFA IgG has been recently commercialized for germ tube antibody detection (CAGTA). This test provides a rapid and simple diagnosis of IC (84.4% sensitivity and 94.7% specificity). The aim of this study is to identify the patients who could be benefited by the use of CAGTA test in critical care setting. Methods: A prospective, cohort, observational multicentre study was carried out in six medical/surgical Intensive care units (ICU) of tertiary-care Spanish hospitals. Candida albicans Germ Tube Antibody test was performed twice a week if predetermined risk factors were present, and serologically demonstrated candidiasis was considered if the testing serum dilution was >= 1: 160 in at least one sample and no other microbiological evidence of invasive candidiasis was found. Results: Fifty-three critically ill non-neutropenic patients (37.7% post surgery) were included. Twenty-two patients (41.5%) had CAGTA-positive results, none of them with positive blood culture for Candida. Neither corrected colonization index nor antifungal treatment had influence on CAGTA results. This finding could corroborate that the CAGTA may be an important biomarker to distinguish between colonization and infection in these patients. The presence of acute renal failure at the beginning of the study was more frequent in CAGTA-negative patients. Previous surgery was statistically more frequent in CAGTA-positive patients. Conclusions: This study identified previous surgery as the principal clinical factor associated with CAGTA-positive results and emphasises the utility of this promising technique, which was not influenced by high Candida colonization or antifungal treatment. Our results suggest that detection of CAGTA may be important for the diagnosis of invasive candidiasis in surgical patients admitted in ICU.

Adaptive Scalable SVD Unit for Fast Processing of Large LSE Problems

Singular Value Decomposition (SVD) is a key linear algebraic operation in many scientific and engineering applications. In particular, many computational intelligence systems rely on machine learning methods involving high dimensionality datasets that have to be fast processed for real-time adaptability. In this paper we describe a practical FPGA (Field Programmable Gate Array) implementation of a SVD processor for accelerating the solution of large LSE problems. The design approach has been comprehensive, from the algorithmic refinement to the numerical analysis to the customization for an efficient hardware realization. The processing scheme rests on an adaptive vector rotation evaluator for error regularization that enhances convergence speed with no penalty on the solution accuracy. The proposed architecture, which follows a data transfer scheme, is scalable and based on the interconnection of simple rotations units, which allows for a trade-off between occupied area and processing acceleration in the final implementation. This permits the SVD processor to be implemented both on low-cost and highend FPGAs, according to the final application requirements.