Detection of Abnormalities in Real-Time Computer Network Traffic Empowered by Machine Learning

This research discloses how to utilize machine learning methods for anomaly detection in real-time on a computer network. While utilizing machine learning for this task is definitely not a novel idea, little literature is about the matter of doing it in real-time. Most machine learning research in PC network anomaly detection depends on the KDD '99 data set and means to demonstrate the proficiency of the algorithms introduced. The emphasis on this data set has caused a lack of scientific papers disclosing how to assemble network data, remove features, and train algorithms for use inreal-time networks. It has been contended that utilizing the KDD '99 dataset for anomaly detection is not appropriate for real-time network systems. This research proposes how the data gathering procedure will be possible utilizing a dummy network and generating synthetic network traffic by analyzing the importance of One-class SVM. As the efficiency of k-means clustering and LTSM neural networks is lower than one-class SVM, that is why this research uses the results of existing research of LSTM and k-means clustering for the comparison with reported outcomes of a similar algorithm on the KDD '99 dataset. Precisely, without engaging KDD ’99 data set by using synthetic network traffic, this research achieved the higher accuracy as compared to the previous researches.

Basal melting at the floating tongue of the 79° North Glacier – on the impacts of ice-shelf basal channels

<p> The floating ice tongue of the 79<sup>°</sup> North Glacier (Nioghalvfjerdsfjorden Glacier) in Northeast Greenland has been found to thin over the past two decades. Recent studies suggest the warming of the ocean as one of the main drivers of destabilizing outlet glaciers of the Greenland ice sheet by enhanced subglacial melting. Using a horizontal two-dimensional numerical plume model, we study the hydrodynamic processes determining basal melt rates beneath the glacial tongue of the 79<sup>°</sup> North Glacier. We specifically investigate the spatial distribution of submarine melting and assess the importance of ice base morphology in controlling basal melting. For our study, we design a suite of simulations by implementing a synthetic network of basal channels. Additionally, we determine the role of subglacial discharge in driving melting along the glacier base. Our model results lead us to the conclusion that channelised basal topographies at the glacier base are the dominant control on the basal melt rates and its spatial distribution. </p>

Distance Entropy Cartography Characterises Centrality in Complex Networks

We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks.