Search

Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to

Unmanned Aerial Vehicle (UAV) networks have emerged as a promising means to provide wireless coverage in open geographical areas. Nevertheless, in wireless networks such as WiFi, signal coverage alone is insufficient to guarantee that network performance meets the quality of service (QoS) requirements of real-time communication services, as it also depends on the traffic load produced by ground users sharing the medium access. We formulate a new problem for UAVs optimal deployment in which the QoS level is guaranteed for real-time voice over WiFi (VoWiFi) communications. More specifically, our goal is to dispatch the minimum number of UAVs possible to provide VoWiFi service to a set of ground users subject to coverage, call-blocking probability, and QoS constraints. Optimal solutions are found using well-known heuristics that include K-means clusterization and genetic algorithms. Via numerical results, we show that the WiFi standard revision (e.g. IEEE 802.11a/b/g/n/ac) in use plays an important role in both coverage and QoS performance and hence, in the number of UAVs required to provide the service.

Drones equipped with wireless network cards can provide communication services in open areas. This paper proposes a hierarchical two-layered network architecture with two types of drones according to their communication equipment: Access and Distribution. While access drones provide WiFi access to ground users, distribution drones act as WiFi-to-5G relay forwarding packets into the 5G Core Network. In this context, we formulate a novel optimization problem for the 3-D initial placement of drones to provide Voice over WiFi (VoWiFi) service to ground users. Our optimization problem finds the minimum number of drones (and their type and location) to be deployed constrained to coverage and minimum voice speech quality. We have used a well-known metaheuristic algorithm (Particle Swarm Optimization) to solve our problem, examining the results obtained for different terrain sizes (from 25m x 25m to 100m x 100m ) and ground users (from 10 to 100 ). In the most demanding case, we were able to provide VoWiFi service with four distribution drones and five access drones. Our results show that the overall number of UAVs deployed grows with the terrain size (i.e., with users’ sparsity) and the number of ground users.

[Abstract]: The level of unpredictability of the COVID-19 pandemics poses a challenge to effectively model its dynamic evolution. In this study we incorporate the inherent stochasticity of the SARS-CoV-2 virus spread by reinterpreting the classical compartmental models of infectious diseases (SIR type) as chemical reaction systems modeled via the Chemical Master Equation and solved by Monte Carlo Methods. Our model predicts the evolution of the pandemics at the level of municipalities, incorporating for the first time (i) a variable infection rate to capture the effect of mitigation policies on the dynamic evolution of the pandemics (ii) SIR-with-jumps taking into account the possibility of multiple infections from a single infected person and (iii) data of viral load quantified by RT-qPCR from samples taken from Wastewater Treatment Plants. The model has been successfully employed for the prediction of the COVID-19 pandemics evolution in small and medium size municipalities of Galicia (Northwest of Spain).

[Abstract]: In the context of phenotype switching and cell fate determination, numerousexperimental studies report hysteresis, despite the fact that the (forward) Chemical Master Equation governing the inherently stochastic underlying gene regulatory networks has a unique steady state (precluding memory effects and hysteresis). In previous works, we demonstrate thathysteresis is a transient phenomenon in systems far from the thermodynamic limit, using the convergence rates of the partial integro-differential equation associated to the forward master equation governing the stochastic process. Here, we make use of the backward master equationto quantify hysteresis and irreversibility based on First Passage Times. First, we derive the backward master equation for a gene regulatory network with protein production in bursts. Solving this equation, we obtain the probability distributions of the first times to reach some fixed final state from one starting state. The mean first passage time provides a measure to quantify how hysteresis and irreversibility in gene regulation at the singlecell level are transient effects that vanish at steady state. In addition, we provide a theoretical basis that reconciles phenotype coexistence and prevalence far from the thermodynamic limit. In fact, we substitute the notion of pseudo-potential (the so-called Waddington landscape) by a time evolving landscape built upon the Chemical Master Equation (CME) in which phenotypes,rather than prevail, persist with different intensities.

The level of unpredictability of the COVID-19 pandemics poses a challenge to effectively model its dynamic evolution. In this study we incorporate the inherent stochasticity of the SARS-CoV-2 virus spread by reinterpreting the classical compartmental models of infectious diseases (SIR type) as chemical reaction systems modeled via the Chemical Master Equation and solved by Monte Carlo Methods. Our model predicts the evolution of the pandemics at the level of municipalities, incorporating for the first time (i) a variable infection rate to capture the effect of mitigation policies on the dynamic evolution of the pandemics (ii) SIR-with-jumps taking into account the possibility of multiple infections from a single infected person and (iii) data of viral load quantified by RT-qPCR from samples taken from Wastewater Treatment Plants. The model has been successfully employed for the prediction of the COVID-19 pandemics evolution in small and medium size municipalities of Galicia (Northwest of Spain).

Assessing the carbon footprint of marine bivalve aquaculture demands an accurate estimation of the CO2 release associated to capital goods and aquaculture operations but also to the metabolic CO2 budget of the cultured species. Nowadays, there are discrepancies on the processes to include in that budget, how to estimate them, and which scale should be applied, from individual to ecosystem. Site-specific environmental conditions and culture methods also affect significantly the estimates. Here, we have gathered environmental, biochemical and metabolic data from published scientific articles, reports and existing databases to present the metabolic CO2 budget for mussel aquaculture in the coastal inlets of the Northwest Iberian upwelling. We analyse the contribution of mussel flesh and shell production jointly and separately. At the individual scale, the shell CO2 budget is estimated from CO2 removal by shell matrix protein synthesis and CO2 release during calcification and respiration to support shell maintenance. Organic carbon in mussel flesh and CO2 released by respiration to support flesh maintenance contribute to the flesh CO2 budget. Only calcification and respiration processes are considered when estimating the metabolic carbon footprint of individual mussels because organic carbon in mussel flesh and shell returns to the atmosphere as CO2 in a relatively short period. While the metabolic carbon footprint associated to mussel shell remains constant at 365 kg CO2 per ton of shell, it varies from 92 to 578 kg CO2 per ton of mussel flesh. This large variability depends on mussel seeding time and harvesting size, due to the differential seasonal growth patterns of flesh and shell. Inclusion of the CO2 potentially immobilised in mussel faeces buried in the sediments would lead to a reduction of the metabolic carbon footprint estimates by up to 6 % compared with the individual estimates

This study presents the results of SARS-CoV-2 surveillance in sewage water of 11 municipalities and marine bioindicators in Galicia (NW of Spain) from May 2020 to May 2021. An integrated pipeline was developed including sampling, pre-treatment and biomarker quantification, RNA detection, SARS-CoV-2 sequencing, mechanistic mathematical modeling and forecasting. The viral load in the inlet stream to the wastewater treatment plants (WWTP) was used to detect new outbreaks of COVID-19, and the data of viral load in the wastewater in combination with data provided by the health system was used to predict the evolution of the pandemic in the municipalities under study within a time horizon of 7 days. Moreover, the study shows that the viral load was eliminated from the treated sewage water in the WWTP, mainly in the biological reactors and the disinfection system. As a result, we detected a minor impact of the virus in the marine environment through the analysis of seawater, marine sediments and, wild and aquacultured mussels in the final discharge point of the WWTP.