Inciting Networks Effects Through Platform Authority: A Design Theory for Service Platforms

The provisioning of software has shifted towards a service model, where applications are hosted, metered, and billed through service platforms rather than being sold as on-premise software. These services can exhibit network effects, which are self-enforcing effects of value generation. They describe the interdependency of service or platform value, service consumption, and third party service provisioning. Service platforms need to be designed to incite these network effects. We propose a design theory which is based on the operator’s platform authority as the key factor for controlling and influencing stakeholders in and around the service platform in different control modes. As a design method, we propose a conceptual modeling language which supports service platform design with staged areas of authority, process flows as well as control methods. We present the rationale behind the language, discuss its application, and propose testable hypotheses for an empirical evaluation

An Information Systems Design Theory for Service Network Effects

Service platforms make software applications available as a service to end users. Platforms enable noticeable economic benefits for scaling and transforming a business. Their long-term competitiveness is ensured in controlled cooperation with channel intermediaries and network partners. Hence, service platforms must be designed to harness self-enforcing effects of value generation, so-called network effects. In an exaptation of existing knowledge, we present an information systems design theory to inform the design of methods that analyze, describe, and guide the design of service platforms through the means of causal loops and control methods. We describe the theory’s purpose and scope as well as the underlying justificatory knowledge behind the constructs and principles of form and function. The design theory covers the design of all service platform participants and activities as well as their transactions and influences in areas of staged platform authority, using enforcing and incentivizing control methods. We demonstrate the principles of implementation with an expository instantiation and apply it to the M-Engineering service platform, which offers surveillance, control, and data acquisition solutions. Furthermore, we present and discuss testable propositions and a study design to evaluate our design principles

MikroTik Devices Landscape, Realistic Honeypots, and Automated Attack Classification

In 2018, several malware campaigns targeted and succeed to infect millions of low-cost routers (malwares e.g., VPN-Filter, Navidade, and SonarDNS). These routers were used, then, for all sort of cybercrimes: from DDoS attacks to ransomware. MikroTik routers are a peculiar example of low-cost routers. These routers are used to provide both last mile access to home users and are used in core network infrastructure. Half of the core routers used in one of the biggest Internet exchanges in the world are MikroTik devices. The problem is that vulnerable firmwares (RouterOS) used in homeusers houses are also used in core networks. In this paper, we are the first to quantify the problem that infecting MikroTik devices would pose to the Internet. Based on more than 4 TB of data, we reveal more than 4 million MikroTik devices in the world. Then, we propose an easy-to-deploy MikroTik honeypot and collect more than 17 millions packets, in 45 days, from sensors deployed in Australia, Brazil, China, India, Netherlands, and the United States. Finally, we use the collected data from our honeypots to automatically classify and assess attacks tailored to MikroTik devices. All our source-codes and analysis are publicly available. We believe that our honeypots and our findings in this paper foster security improvements in MikroTik devices worldwide

Characterising attacks targeting low-cost routers: a MikroTik case study (Extended)

Attacks targeting network infrastructure devices pose a threat to the security of the internet. An attack targeting such devices can affect an entire autonomous system. In recent years, malware such as VPNFilter, Navidade, and SonarDNS has been used to compromise low-cost routers and commit all sorts of cybercrimes from DDoS attacks to ransomware deployments. Routers of the type concerned are used both to provide last-mile access for home users and to manage interdomain routing (BGP). MikroTik is a particular brand of low-cost router. In our previous research, we found more than 4 million MikroTik routers available on the internet. We have shown that these devices are also popular in Internet Exchange infrastructures. Despite their popularity, these devices are known to have numerous vulnerabilities. In this paper, we extend our previous analysis by presenting a long-term investigation of MikroTik-targeted attacks. By using a highly interactive honeypot that we developed, we collected more than 44 million packets over 120 days, from sensors deployed in Australia, Brazil, China, India, the Netherlands, and the United States. The incoming traffic was classified on the basis of Common Vulnerabilities and Exposures to detect attacks targeting MikroTik devices. That enabled us to identify a wide range of activities on the system, such as cryptocurrency mining, DNS server redirection, and more than 3,000 successfully established tunnels used for eavesdropping. Although this research focuses on Mikrotik devices, both the methodology and the publicly available scripts can be easily applied to any other type of network device

Performance under pressure in skill tasks: An analysis of professional darts

Ötting M, Deutscher C, Schneemann S, Langrock R, Gehrmann S, Scholten H. Performance under pressure in skill tasks: An analysis of professional darts. PLOS ONE. 2020;15(2): e0228870.Understanding and predicting how individuals perform in high-pressure situations is of importance in designing and managing workplaces. We investigate performance under pressure in professional darts as a near-ideal setting with no direct interaction between players and a high number of observations per subject. Analyzing almost one year of tournament data covering 32,274 dart throws, we find no evidence in favor of either choking or excelling under pressure

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers