System Estimates of Cyclical Unemployment and Cyclical Output in the 15 European Union Member-States, 1961-1999

The purpose of this paper was to estimate cyclical unemployment and cyclical output in the 15 European Union member-states using a system of Phillips curve and Okun’s law equations. Treating both the NAIRU and the potential output growth rate as time varying unobserved stochastic processes, a state-space maximum likelihood estimation method - using Kalman filter where the state variables were random walks - was followed in order to estimate the 15 systems of equations. Overall, the estimated with the new approach systems of conditional equations suggested that the extent and direction of changes of cyclical unemployment and cyclical output over the period 1961-1999 is mixed across the 15 EU member states. The paper concludes that the application of “common” policies across the 15 EU member states may be questionable because of the different expected effects of these policies on the various economies.Phillips curve, Okun’s law, Kalman filter, Cyclical unemployment, Potential output growth rate, NAIRU, Europe

Automated key exchange protocol evaluation in delay tolerant networks

Cryptographic key exchange is considered to be a challenging problem in Delay Tolerant Networks (DTNs) operating in deep space environments. The difficulties and challenges are attributed to the peculiarities and constraints of the harsh communication conditions DTNs typically operate in, rather than the actual features of the underlying key management cryptographic protocols and solutions. In this paper we propose a framework for evaluation of key ex- change protocols in a DTN setting. Our contribution is twofold as the proposed framework can be used as a decision making tool for automated evaluation of various communication scenarios with regards to routing decisions and as part of a method for protocol evaluation in DTNs

Towards a Threat Intelligence Informed Digital Forensics Readiness Framework

Digital Forensic Readiness (DFR) has received little attention by the research community, when compared to the core digital forensic investigation processes. DFR was primarily about logging of security events to be leveraged by the forensic analysis phase. However, the increasing number of security incidents and the overwhelming volumes of data produced mandate the development of more effective and efficient DFR approaches. We propose a DFR framework focusing on the prioritisation, triaging and selection of Indicators of Compromise (IoC) to be used in investigations of security incidents. A core component of the framework is the contextualisation of the IoCs to the underlying organisation, which can be achieved with the use of clustering and classification algoriihms and a local IoC database

Exploring the protection of private browsing in desktop browsers

Desktop browsers have introduced private browsing mode, a security control which aims to protect users’ data that are generated during a private browsing session, by not storing them in the file system. As the Internet becomes ubiquitous, the existence of this security control is beneficial to users,since privacy violations are increasing, while users tend to be more concerned about their privacy when browsing the web in a post-Snowden era. In this context, this work examines the protection that is offered by the private browsing mode of the most popular desktop browsers in Windows (i.e.,Chrome, Firefox, IE and Opera).Our experiments uncover occasions in which even if users browse the web with a private session,privacy violations exist contrary to what is documented by the browser.To raise the bar of privacy protection that is offered by web browsers,we propose the use of a virtual filesystem as the storage medium of browsers’ cache data. We demonstrate with a case study how this countermeasure protects users from the privacy violations, which are previously identified in this work

Automated Mortality Prediction in Critically-ill Patients with Thrombosis using Machine Learning

Venous thromboembolism (VTE) is the third most common cardiovascular condition. Some high risk patients diagnosed with VTE need immediate treatment and monitoring in intensive care units (ICU) as the mortality rate is high. Most of the published predictive models for ICU mortality give information on in-hospital mortality using data recorded in the first day of ICU admission. The purpose of the current study is to predict in-hospital and after-discharge mortality in patients with VTE admitted to ICU using a machine learning (ML) framework. We studied 2,468 patients from the Medical Information Mart for Intensive Care (MIMIC-III) database, admitted to ICU with a diagnosis of VTE. We formed ML classification tasks for early and late mortality prediction. In total, 1,471 features were extracted for each patient, grouped in seven categories each representing a different type of medical assessment. We used an automated ML platform, JADBIO, as well as a class balancing combined with a Random Forest classifier, in order to evaluate the importance of class imbalance. Both methods showed significant ability in prediction of early mortality (AUC=0.92). Nevertheless, the task of predicting late mortality was less efficient (AUC=0.82). To the best of our knowledge, this is the first study in which ML is used to predict short-term and long-term mortality for ICU patients with VTE based on a multitude of clinical features collected over time

Human-Centered Specification Exemplars for Critical Infrastructure Environments

Specification models of critical infrastructure focus on parts of a larger environment. However, to consider the security of critical infrastructure systems, we need approaches for modelling the sum of these parts; these include people and activities, as well as technology. This paper present human-centered specification exemplars that capture the nuances associated with interactions between people, technology, and critical infrastructure environments. We describe requirements each exemplar needs to satisfy, and present preliminary results developing and evaluating them

ISUMS: Indoor Space Usage Monitoring System for Sustainable Built Environment Using LoRaWAN

In this work we investigate how IoT in conjunction with the data-driven Circular Economy (CE) model can contribute towards a more sustainable Built Environment (). We address longstanding challenges related to the distribution of resources and the multi-sectoral impact of the buildings sector. We first discuss recent developments in policy making that underpin the recently introduced Green Deal by the European Commission and the paradigm of Circular Economy. This motivates the development of ISUMS; an Indoor Space Usage Monitoring System. The system provides the facilities and the estates management teams of commercial and office buildings with an IoT-enabled system able to provide fine grained and timely data on occupancy rates of shared building spaces. This type of data can then be used to develop new or inform existing action plans towards increasing building sustainability. The development of the system comprises a) a Pre-Analysis Plan (PAP) for a smart campus use case at the Talbot Campus of Bournemouth University; b) a proof of concept IoT end-device that can be integrated in pieces of furniture for occupancy monitoring; and c) a measurements campaign for evaluating the use of LoRaWAN in indoor environments. ISUMS expands the notions of smart buildings and buildings management beyond interconnected actuators and towards adaptive space management with dynamic changes in the use requirements

Vulnerability Exposure Driven Intelligence in Smart, Circular Cities

In this paper we study the vulnerability management dimension in smart city initiatives. As many cities across the globe invest a considerable amount of effort, resources and budget to modernise their infrastructure by deploying a series of technologies such as 5G, Software Defined Networks and IoT, we conduct an empirical analysis of their current exposure to existing vulnerabilities. We use an updated vulnerability dataset which is further enriched by quantitative research data from independent studies evaluating the maturity and accomplishments of cities in their journey to become smart. We particularly focus on cities that aspire to implement a (data-driven) Circular Economy agenda which we consider to potentially yield the highest risk from a vulnerabilities exposure perspective. Findings show that although a smarter city is attributed with a higher vulnerability exposure, investments on technology and human capital moderate this exposure in a way that it can be reduced

Actionable Threat Intelligence for Digital Forensics Readiness

The purpose of this paper is to formulate a novel model for enhancing the effectiveness of existing Digital Forensic Readiness (DFR) schemes by leveraging the benefits of cyber threat information sharing. This paper employs a quantitative methodology to identify the most popular Threat Intelligence elements and introduces a formalized procedure to correlate these elements with potential digital evidence resulting in the quick and accurate identification of patterns of malware activities. While threat intelligence exchange steadily becomes a common practice for the prevention or detection of security incidents, the proposed approach highlights its usefulness for the digital forensics domain. The proposed model can help organizations to improve their digital forensic readiness posture and thus minimize the time and cost of cybercrime incident

Improving Forensic Triage Efficiency through Cyber Threat Intelligence

The complication of information technology and the proliferation of heterogeneous security devices that produce increased volumes of data coupled with the ever-changing threat landscape challenges have an adverse impact on the efficiency of information security controls and digital forensics, as well as incident response approaches. Cyber Threat Intelligence (CTI)and forensic preparedness are the two parts of the so-called managed security services that defendants can employ to repel, mitigate or investigate security incidents. Despite their success, there is no known effort that has combined these two approaches to enhance Digital Forensic Readiness (DFR) and thus decrease the time and cost of incident response and investigation. This paper builds upon and extends a DFR model that utilises actionable CTI to improve the maturity levels of DFR. The effectiveness and applicability of this model are evaluated through a series of experiments that employ malware-related network data simulating real-world attack scenarios. To this extent, the model manages to identify the root causes of information security incidents with high accuracy (90.73%), precision (96.17%) and recall (93.61%), while managing to decrease significantly the volume of data digital forensic investigators need to examine. The contribution of this paper is twofold. First, it indicates that CTI can be employed by digital forensics processes. Second, it demonstrates and evaluates an efficient mechanism that enhances operational DFR