ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Datasets

The Internet of Things (IoT) is reshaping our connected world as the number of lightweight devices connected to the Internet is rapidly growing. Therefore, high-quality research on intrusion detection in the IoT domain is essential. To this end, network intrusion datasets are fundamental, as many attack detection strategies have to be trained and evaluated using such datasets. In this paper, we introduce the description, statistical analysis, and machine learning evaluation of the novel ToN_IoT dataset. Comparison to other recent IoT datasets shows the importance of heterogeneity within these datasets, and how differences between datasets may have a huge impact on detection performance. In a cross-training experiment, we show that the inclusion of different data collection methods and a large diversity of the monitored features are of crucial importance for IoT network intrusion datasets to be useful for the industry. We also explain that the practical application of IoT datasets in operational environments requires the standardization of feature descriptions and cyberattack classes. This can only be achieved with a joint effort from the research community

A rapid review of the rate of attrition from the health workforce

Attrition or losses from the health workforce exacerbate critical shortages of health workers and can be a barrier to countries reaching their universal health coverage and equity goals. Despite the importance of accurate estimates of the attrition rate (and in particular the voluntary attrition rate) to conduct effective workforce planning, there is a dearth of an agreed definition, information and studies on this topic. We conducted a rapid review of studies published since 2005 on attrition rates of health workers from the workforce in different regions and settings; 1782 studies were identified, of which 51 were included in the study. In addition, we analysed data from the State of the World's Midwifery (SoWMy) 2014 survey and associated regional survey for the Arab states on the annual voluntary attrition rate for sexual, reproductive, maternal and newborn health workers (mainly midwives, doctors and nurses) in the 79 participating countries. There is a diversity of definitions of attrition and barely any studies distinguish between total and voluntary attrition (i.e. choosing to leave the workforce). Attrition rate estimates were provided for different periods of time, ranging from 3 months to 12 years, using different calculations and data collection systems. Overall, the total annual attrition rate varied between 3 and 44% while the voluntary annual attrition rate varied between 0.3 to 28%. In the SoWMy analysis, 49 countries provided some data on voluntary attrition rates of their SRMNH cadres. The average annual voluntary attrition rate was 6.8% across all cadres. Attrition, and particularly voluntary attrition, is under-recorded and understudied. The lack of internationally comparable definitions and guidelines for measuring attrition from the health workforce makes it very difficult for countries to identify the main causes of attrition and to develop and test strategies for reducing it. Standardized definitions and methods of measuring attrition are required