Intrusion detection systems (IDSs) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles are proposed. A hybrid feature selection technique comprising three methods, i.e., particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensemble based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. The results regarding the UNSW-NB15 dataset also improve the ones achieved by several state-of-the-art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by the IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier.INDEX TERMS Two-stage meta classifier, network anomaly detection, hybrid feature selection, intrusion detection system, statistical significance test.

Purpose. While it is commonly recognised that Big Data have an immense potential to generate value for business organisations, appropriating value from Big Data and, in particular, Big Data-enabled analytics is still an open issue for many organisations. This paper develops a maturity model to support organisations in the realisation of the value created by Big Data. Design/Methodology/Approach. The maturity model is developed following a qualitative approach based on literature analysis and semi-structured interviews with domain experts. The completeness and usefulness of the model are evaluated qualitatively by practitioners, whereas the applicability of the model is evaluated by Big Data maturity assessments in three real world organisations. Findings. The proposed maturity model is considered exhaustive by domain experts and has helped the three assessed organisations to develop a more critical understanding of the next steps to take. Originality/Value. The maturity model integrates existing industry-developed maturity models into one single coherent Big Data maturity model. The proposed model answers the call for research on Big Data to abstract from technical issues to focus on the business implications of Big Data initiatives.

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Quality of service (QoS) can be a critical element for achieving the business goals of a service provider, for the acceptance of a service by the user, or for guaranteeing service characteristics in a composition of services, where a service is defined as either a software or a software-support (i.e., infrastructural) service which is available on any type of network or electronic channel. The goal of this article is to compare the approaches to QoS description in the literature, where several models and metamodels are included. consider a large spectrum of models and metamodels to describe service quality, ranging from ontological approaches to define quality measures, metrics, and dimensions, to metamodels enabling the specification of quality-based service requirements and capabilities as well as of SLAs (Service-Level Agreements) and SLA templates for service provisioning. Our survey is performed by inspecting the characteristics of the available approaches to reveal which are the consolidated ones and which are the ones specific to given aspects and to analyze where the need for further research and investigation lies. The approaches here illustrated have been selected based on a systematic review of conference proceedings and journals spanning various research areas in computer science and engineering, including: distributed, information, and telecommunication systems, networks and security, and service-oriented and grid computing.