Intrusion detection systems for smart home IoT devices: experimental comparison study

Smart homes are one of the most promising applications of the emerging Internet of Things (IoT) technology. With the growing number of IoT related devices such as smart thermostats, smart fridges, smart speaker, smart light bulbs and smart locks, smart homes promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks and home privacy breaches. In order to overcome such risks, Intrusion Detection Systems are presented as pertinent tools that can provide network-level protection for smart devices deployed in home environments. These systems monitor the network activities of the smart home-connected de-vices and focus on alerting suspicious or malicious activity. They also can deal with detected abnormal activities by hindering the impostors in accessing the victim devices. However, the employment of such systems in the context of a smart home can be challenging due to the devices hardware limitations, which may restrict their ability to counter the existing and emerging attack vectors. Therefore, this paper proposes an experimental comparison between the widely used open-source NIDSs namely Snort, Suricata and Bro IDS to find the most appropriate one for smart homes in term of detection accuracy and resources consumption including CP and memory utilization. Experimental Results show that Suricata is the best performing NIDS for smart homesComment: 7 pages, 4 figures, 2 table

Patients with Asthma and Comorbid Allergic Rhinitis: Is Optimal Quality of Life Achievable in Real Life?

Asthma trials suggest that patients reaching total disease control have an optimal Health Related Quality of Life (HRQoL). Moreover, rhinitis is present in almost 80% of asthmatics and impacts asthma control and patient HRQoL. We explored whether optimal HRQoL was reachable in a real-life setting, and evaluated the disease and patient related patterns associated to optimal HRQoL achievement. = 7.617; p<0.006).Approximately one third of the patients in our survey were found to have an optimal HRQoL. While unsatisfactory disease control was the primary reason why the remainder failed to attain optimal HRQoL, it is clear that illness perception and mood also played parts. Therefore, therapeutic plans should be directed not only toward achieving the best possible clinical control of asthma and comorbid rhinitis, but also to incorporating individualized elements according to patient-related characteristics

Pulmonary vascular research institute GoDeep: a meta-registry merging deep phenotyping datafrom international PH reference centers

The Pulmonary Vascular Research Institute GoDeep meta-registry is a collaboration of pulmonary hypertension (PH) reference centers across the globe. Merging worldwide PH data in a central meta-registry to allow advanced analysis of the heterogeneity of PH and its groups/subgroups on a worldwide geographical, ethnical, and etiological landscape (ClinTrial. gov NCT05329714). Retrospective and prospective PH patient data (diagnosis based on catheterization; individuals with exclusion of PH are included as a comparator group) are mapped to a common clinical parameter set of more than 350 items, anonymized and electronically exported to a central server. Use and access is decided by the GoDeep steering board, where each center has one vote. As of April 2022, GoDeep comprised 15,742 individuals with 1.9 million data points from eight PH centers. Geographic distribution comprises 3990 enrollees (25%) from America and 11,752 (75%) from Europe. Eighty-nine perecent were diagnosed with PH and 11% were classified as not PH and provided a comparator group. The retrospective observation period is an average of 3.5 years (standard error of the mean 0.04), with 1159 PH patients followed for over 10 years. Pulmonary arterial hypertension represents the largest PH group (42.6%), followed by Group 2 (21.7%), Group 3 (17.3%), Group 4 (15.2%), and Group 5 (3.3%). The age distribution spans several decades, with patients 60 years or older comprising 60%. The majority of patients met an intermediate risk profile upon diagnosis. Data entry from a further six centers is ongoing, and negotiations with >10 centers worldwide have commenced. Using electronic interface-based automated retrospective and prospective data transfer, GoDeep aims to provide in-depth epidemiological and etiological understanding of PH and its various groups/subgroups on a global scale, offering insights for improved management

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge