The current paper addresses relevant network security vulnerabilities introduced by network devices within the emerging paradigm of Internet of Things (IoT) as well as the urgent need to mitigate the negative effects of some types of Distributed Denial of Service (DDoS) attacks that try to explore those security weaknesses. We design and implement a Software-Defined Intrusion Detection System (IDS) that reactively impairs the attacks at its origin, ensuring the “normal operation” of the network infrastructure. Our proposal includes an IDS that automatically detects several DDoS attacks, and then as an attack is detected, it notifies a Software Defined Networking (SDN) controller. The current proposal also downloads some convenient traffic forwarding decisions from the SDN controller to network devices. The evaluation results suggest that our proposal timely detects several types of cyber-attacks based on DDoS, mitigates their negative impacts on the network performance, and ensures the correct data delivery of normal traffic. Our work sheds light on the programming relevance over an abstracted view of the network infrastructure to timely detect a Botnet exploitation, mitigate malicious traffic at its source, and protect benign traffic.

Companies seek to promote a swift digitalization of their business processes and new disruptive features to gain an advantage over their competitors. This often results in a wider attack surface that may be exposed to exploitation from adversaries. As budgets are thin, one of the most popular security solutions CISOs choose to invest in is Network-based Intrusion Detection Systems (NIDS). As anomaly-based NIDS work over a baseline of normal and expected activity, one of the key areas of development is the training of deep learning classification models robust enough so that, given a different network context, the system is still capable of high rate accuracy for intrusion detection. In this study, we propose an anomaly-based NIDS using a deep learning stacked-LSTM model with a novel pre-processing technique that gives it context-free features and outperforms most related works, obtaining over 99% accuracy over the CICIDS2017 dataset. This system can also be applied to different environments without losing its accuracy due to its basis on context-free features. Moreover, using synthetic network attacks, it has been shown that this NIDS approach can detect specific categories of attacks.

Data sharing in the health sector represents a big problem due to privacy and security issues. Health data have tremendous value for organisations and criminals. The European Commission has classified health data as a unique resource owing to their ability to enable both retrospective and prospective research at a low cost. Similarly, the Organisation for Economic Co-operation and Development (OECD) encourages member nations to create and implement health data governance systems that protect individual privacy while allowing data sharing. This paper proposes adopting a blockchain framework to enable the transparent sharing of medical information among health entities in a secure environment. We develop a laboratory-based prototype using a design science research methodology (DSRM). This approach has its roots in the sciences of engineering and artificial intelligence, and its primary goal is to create relevant artefacts that add value to the fields in which they are used. We adopt a patient-centric approach, according to which a patient is the owner of their data and may allow hospitals and health professionals access to their data.

Security and Privacy Issues of Big Data: 10.4018/978-1-4666-8505-5.ch002: This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably

With the emergence of digital transformation, there is an increasing need for Chinese construction companies to adopt digital procurement (D-procurement). However, there is a lack of theoretical foundation to guide and support the adoption practices. This study aims to fill the research gap through the provision of a model by grouping a set of relevance-based critical success factors (CSFs) into the Technology–Organisation–Environment (TOE) framework for D-procurement adoption success (DAS). A case study approach is applied in the research. We selected H Group as it is one of the most representative D-procurement cases in China. The study includes two parts. In the first part, a systematic literature review was conducted, and 17 CSFs were identified from 12 selected studies. By grouping the 17 CSFs into the TOE framework, we put forward a basic CSF–TOE model. In the second part, an in-depth interview was carried out in H Group, where the 17 selected experts were asked to rank the previously identified CSF. Based on their order of relevance, the 17 CSFs were re-organised in the basic CSF–TOE model, and a relevance-based CSF–TOE model was finally proposed. This study is vital for D-procurement adoption because most existing CSF studies are based on the literature and questionnaire surveys, and there is a lack of actual case studies. In addition, this study significantly contributes to the field of D-procurement adoption for construction companies by providing a theoretical framework for practice and a relevance-based CSF–TOE model for research.

Organizations have become increasingly dependent on information systems to perform their business as usual activities. Moreover, organizations have registered an increase in the number of cyber-attacks, namely: industrial espionage, confidential information leakage, digital theft or pure damage to corporate image and reputation. In order to try to mitigate these issues, organizations like the National Institute of Standards and Technology (NIST) have made an effort to establish a cybersecurity protection guide. This paper presents a baseline for developing a generic and flexible model for manipulating key factors inside organizations: Processes, Human Resources and Technology, and extrapolate the percentage of compliance with the NIST cybersecurity framework, measure the current cybersecurity risk and allocate financial investments towards specific compliance objectives and reduce the overlapping of existing resources.