LogoMotive: Detecting Logos on Websites to Identify Online Scams - A TLD Case Study

Logos give a website a familiar feel and promote trust. Scammers take advantage of that by using well-known organizations’ logos on malicious websites. Unsuspecting Internet users see these logos and think they are looking at a government website or legitimate webshop, when it is a phishing site, a counterfeit webshop, or a site set up to spread misinformation. We present the largest logo detection study on websites to date. We analyze 6.2M domain names from the Netherlands ’ country-code top-level domain.nl, in two case studies to detect logo misuse for two organizations: the Dutch national government and Thuiswinkel Waarborg, an organization that issues certified webshop trust marks. We show how we can detect phishing, spear phishing, dormant phishing attacks, and brand misuse. To that end, we developed LogoMotive, an application that crawls domain names, generates screenshots, and detects logos using supervised machine learning. LogoMotive is operational in the.nl registry, and it is generalizable to detect any other logo in any DNS zone to help identify abuse.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

Personalized support for well-being at work: an overview of the SWELL project

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense–reason–act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.Applied Ergonomics and DesignInteractive Intelligenc