Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Anomaly detection for IoT networks using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning. This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets. This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%. This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature

Two-tier Intrusion Detection System for Mobile Ad Hoc Networks

Nowadays, a commonly used wireless network (i.e. Wi-Fi) operates with the aid of a fixed infrastructure (i.e. an access point) to facilitate communication between nodes when they roam from one location to another. The need for such a fixed supporting infrastructure limits the adaptability of the wireless network, especially in situations where the deployment of such an infrastructure is impractical. In addition, Wi-Fi limits nodes' communication as it only provides facility for mobile nodes to send and receive information, but not reroute the information across the network. Recent advancements in computer network introduced a new wireless network, known as a Mobile Ad Hoc Network (MANET), to overcome these limitations. MANET has a set of unique characteristics that make it different from other kind of wireless networks. Often referred as a peer to peer network, such a network does not have any fixed topology, thus nodes are free to roam anywhere, and could join or leave the network anytime they desire. Its ability to be setup without the need of any infrastructure is very useful, especially in geographically constrained environments such as in a military battlefield or a disaster relief operation. In addition, through its multi hop routing facility, each node could function as a router, thus communication between nodes could be made available without the need of a supporting fixed router or an access point. However, these handy facilities come with big challenges, especially in dealing with the security issues. This research aims to address MANET security issues by proposing a novel intrusion detection system that could be used to complement existing prevention mechanisms that have been proposed to secure such a network. A comprehensive analysis of attacks and the existing security measures proved that there is a need for an Intrusion Detection System (IDS) to protect MANETs against security threats. The analysis also suggested that the existing IDS proposed for MANET are not immune against a colluding blackmail attack due to the nature of such a network that comprises autonomous and anonymous nodes. The IDS architecture as proposed in this study utilises trust relationships between nodes to overcome this nodes' anonymity issue. Through a friendship mechanism, the problems of false accusations and false alarms caused by blackmail attackers in global detection and response mechanisms could be eliminated. The applicability of the friendship concept as well as other proposed mechanisms to solve MANET IDS related issues have been validated through a set of simulation experiments. Several MANET settings, which differ from each other based on the network's density level, the number of initial trusted friends owned by each node, and the duration of the simulation times, have been used to study the effects of such factors towards the overall performance of the proposed IDS framework. The results obtained from the experiments proved that the proposed concepts are capable to at least minimise i f not fully eliminate the problem currently faced in MANET IDS