xPF: Packet Filtering for Low-Cost Network Monitoring

The ever-increasing complexity in network infrastructures is making critical the demand for network monitoring tools. While the majority of network operators rely on low-cost open-source tools based on commodity hardware and operating systems, the increasing link speeds and complexity of network monitoring applications have revealed inefficiencies in the existing software organization, which may prohibit the use of such tools in high-speed networks. Although several new architectures have been proposed to address these problems, they require significant effort in re-engineering the existing body of applications. We present an alternative approach that addresses the primary sources of inefficiency without significantly altering the software structure. Specifically, we enhance the computational model of the Berkeley packet filter (BPF) to move much of the processing associated with monitoring into the kernel, thereby removing the overhead associated with context switching between kernel and applications. The resulting packet filter, called xPF, allows new tools to be more efficiently implemented and existing tools to be easily optimized for high-speed networks. We present the design and implementation of xPF as well as several example applications that demonstrate the efficiency of our approach

MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

Pattern-driven security, privacy, dependability and interoperability management of iot environments

Achieving Security, Privacy, Dependability and Interoperability (SPDI) is of paramount importance for the ubiquitous deployment and impact maximization of Internet of Things (IoT) applications. Nevertheless, said requirements are not only difficult to achieve at system initialization, but also hard to prove and maintain at run-time. This paper highlights an approach to tackling the above challenges, through the definition of pattern language and a framework that can guarantee SPDI in IoT orchestrations. By integrating pattern reasoning engines at the various layers of the IoT infrastructure, and a machine-processable representation of said pattern through Drools rules, the proposed framework can provide ways to fulfill SPDI requirements at design time, and also provide the means to guarantee those SPDI properties and manage the orchestrations accordingly. Moreover, an application example of the framework is presented in an Industrial IoT monitoring environment

The impact of comorbid impulsive/compulsive disorders on problematic internet use

Background and Aims: Problematic Internet Use (PIU) is commonplace but is not yet recognized as a formal mental disorder. Excessive Internet use could result from other conditions such as gambling disorder. The aim of the study was to assess the impact of impulsive-compulsive comorbidities on the presentation of PIU, defined using Young’s Diagnostic Questionnaire. Methods: 123 adults aged 18-29 years were recruited using media advertisements, and attended the research center for a detailed psychiatric assessment, including interviews, completion of questionnaires and neuropsychological testing. Participants were classified into three groups: PIU with no comorbid impulsive/compulsive disorders (n=18), PIU with one or more comorbid impulsive/compulsive disorders (n=37), and healthy controls (n=67). Differences between the three groups were characterized in terms of demographic, clinical, and cognitive variables. Effect sizes for overall effects of group were also reported. Results: The three groups did not differ significantly on age, gender, levels of education, nicotine consumption, or alcohol use (small effect sizes). Quality of life was significantly impaired in PIU irrespective of whether or not individuals had comorbid impulsive/compulsive disorders (large effect size). However, impaired response inhibition and decision-making were only identified in PIU with impulsive/compulsive comorbidities (medium effect sizes)

Why Current Publication Practices May Distort Science

John Ioannidis and colleagues argue that the current system of publication in biomedical research provides a distorted view of the reality of scientific data