Search

The aim of this study was to evaluate the possible correlation between biometric indices (length and weight) and some blood parameters in 225 Gilthead sea bream (Sparus aurata)and 225 European sea bass (Dicentrarchus labrax)cultured in onshore tanks with the same physical–chemical conditions of water (temperature, soluble oxygen and salinity). From each fish, blood samples were collected to evaluate Red Blood Cell Count, Haematocrit, Haemoglobin Concentration, White Blood Cell Count, Thrombocyte Count, Mean Corpuscular Volume, Mean Corpuscular Haemoglobin and Mean Corpuscular Haemoglobin Concentration, glucose, lactate and Total Proteins. In both species, the relationship between the biometric indices and blood parameters was evaluated using linear regression analysis that showed a statistically significant correlation between the biometric indices and seven of the studied parameters. These results showed that fish size is an important factor in the interpretation of haematological and biochemical profiles in order to evaluate correctly the health status of the fish. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: (i) the presence of so many features leads to lengthy training processes (particularly when features are highly correlated), while prediction accuracy does not proportionally improve; (ii) some of the features may introduce bias during the classification process, particularly those that have scarce relation with the data traffic to be classified. To this end, by reducing the feature space and retaining only the most significant features, Feature Selection (FS) becomes a crucial pre-processing step in network management and, specifically, for the purposes of network intrusion detection. In this review paper, we complement other surveys in multiple ways: (i) evaluating more recent datasets (updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch Python-based procedure; (ii) providing a synopsis of most credited FS approaches in the field of intrusion detection, including Multi-Objective Evolutionary techniques; (iii) assessing various experimental analyses such as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines to network/security managers who are considering the incorporation of ML concepts into network intrusion detection, where trade-offs between performance and resource consumption are crucial. [ABSTRACT FROM AUTHOR]