Edinburgh Napier University

About: Edinburgh Napier University is a education organization based out in Edinburgh, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2665 authors who have published 6859 publications receiving 175272 citations.

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.

Abstract: In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

R-wave detection using continuous wavelet modulus maxima

Abstract: Modulus maxima derived from the continuous wavelet transform offers an enhanced time-frequency analysis technique for ECG signal analysis. Features within the ECG can be shown to correspond to various morphologies in the continuous modulus maxima domain. This domain has an easy interpretation and offers a good tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of these properties we have developed an R-wave detector and tested it using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.7% and a positive predictive value of 99.68%).

Quality control of solar radiation and sunshine measurements – lessons learnt from processing worldwide databases

Abstract: As a result of an exponential increase in the application of solar water heating, and more recently solar PV systems, measurement of the available solar radiation resource is gaining rapid momentum. New databases are being created and many of these are being made available via Internet. Up-to-date series of these data are also being sought by architects and building services professionals for better design of buildings. However, in many instances due care is not being exercised with respect to quality control of the measured dataset. Two major activities that utilized large databases of solar radiation, sunshine and cloud-cover have recently been concluded within Europe – the European Solar Radiation Atlas and the Chartered Institution of Building Services Engineers (London) Guide on ‘Weather and Solar Data’. This article provides an account of the research undertaken towards development of robust techniques for identi” cation of erroneous data and their substitution using validated methods for generating...