Clust-IT:Clustering-Based Intrusion Detection in IoT Environments

Low-powered and resource-constrained devices are forming a greater part of our smart networks. For this reason, they have recently been the target of various cyber-attacks. However, these devices often cannot implement traditional intrusion detection systems (IDS), or they can not produce or store the audit trails needed for inspection. Therefore, it is often necessary to adapt existing IDS systems and malware detection approaches to cope with these constraints. We explore the application of unsupervised learning techniques, specifically clustering, to develop a novel IDS for networks composed of low-powered devices. We describe our solution, called Clust-IT (Clustering of IoT), to manage heterogeneous data collected from cooperative and distributed networks of connected devices and searching these data for indicators of compromise while remaining protocol agnostic. We outline a novel application of OPTICS to various available IoT datasets, composed of both packet and flow captures, to demonstrate the capabilities of the proposed techniques and evaluate their feasibility in developing an IoT IDS

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Linguistically Informed Information Retrieval for Contact Center Automation

Customer service departments need to handle an increasing volume of textual data in the form of electronic mail. To handle this volume, some kind of automated processing is required. The aim of the research described in this thesis is to employ techniques from the fields of information retrieval (IR) and natural language processing (NLP) to automate part of the customer service pipeline.

War in Parliament: What a Digital Approach Can Add to the Study of Parliamentary History

With the digitization of the parliamentary proceedings (Handelingen der Staten Generaal), the structuring of this body of data, and the development of an advanced search engine, we can apply new methods of historical research. This contributes to a further promotion of the sophisticated use of quantitative data to enhance qualitative historical research. This article focuses on the Boerenpartij(Farmers’ Party), the first political party from the far right that entered Dutch parliament after the Second World War (WWII). The Boerenpartij is remembered as being stigmatized by the traditional political parties as "wrong" (" fout "), as National Socialism and its supporters were dubbed in the Netherlands. However, no systematic research has been conducted on the questions: in what way, how frequently and for what purpose these connections with the "wrong" past were made. With the available digitized data and the retrieval techniques offered by computer scientists it is now possible to answer these questions