Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Renal replacement therapy in acute kidney injury: controversy and consensus

Renal replacement therapies (RRTs) represent a cornerstone in the management of severe acute kidney injury. This area of intensive care and nephrology has undergone significant improvement and evolution in recent years. Continuous RRTs have been a major focus of new technological and treatment strategies. RRT is being used increasingly in the intensive care unit, not only for renal indications but also for other organ-supportive strategies. Several aspects related to RRT are now well established, but others remain controversial. In this review, we review the available RRT modalities, covering technical and clinical aspects. We discuss several controversial issues, provide some practical recommendations, and where possible suggest a research agenda for the future

Ecological status assessment of European lakes: A comparison of metrics for phytoplankton, macrophytes, benthic invertebrates and fish

Data on phytoplankton, macrophytes, benthic invertebrates and fish from more than 2000 lakes in 22 European countries were used to develop and test metrics for assessing the ecological status of European lakes as required by the Water Framework Directive. The strongest and most sensitive of the 11 metrics responding to eutrophication pressure were phytoplankton chlorophyll a, a taxonomic composition trophic index and a functional traits index, the macrophyte intercalibration taxonomic composition metric and a Nordic lake fish index. Intermediate response was found for a cyanobacterial bloom intensity index (Cyano), the Ellenberg macrophyte index and a multimetric index for benthic invertebrates. The latter also responded to hydromorphological pressure. The metrics provide information on primary and secondary impacts of eutrophication in the pelagic and the littoral zone of lakes. Several of these metrics were used as common metrics in the intercalibration of national assessment systems or have been incorporated directly into the national systems. New biological metrics have been developed to assess hydromorphological pressures, based on aquatic macrophyte responses to water level fluctuations, and on macroinvertebrate responses to morphological modifications of lake shorelines. These metrics thus enable the quantification of biological impacts of hydromorphological pressures in lakes.Additional co-authors: Christine Argillier, Erik Jeppesen, Torben L. Lauridsen, Sandra Poikan