Collecting metadata from Transport Layer Security (TLS) servers on a large scale allows to draw conclusions about their capabilities and configuration. This provides not only insights into the Internet but it enables use cases like detecting malicious Command and Control (C &C) servers. However, active scanners can only observe and interpret the behavior of TLS servers, the underlying configuration and implementation causing the behavior remains hidden. Existing approaches struggle between resource intensive scans that can reconstruct this data and light-weight fingerprinting approaches that aim to differentiate servers without making any assumptions about their inner working. With this work we propose DissecTLS, an active TLS scanner that is both light-weight enough to be used for Internet measurements and able to reconstruct the configuration and capabilities of the TLS stack. This was achieved by modeling the parameters of the TLS stack and derive an active scan that dynamically creates scanning probes based on the model and the previous responses from the server. We provide a comparison of five active TLS scanning and fingerprinting approaches in a local testbed and on toplist targets. We conducted a measurement study over nine weeks to fingerprint C &C servers and analyzed popular and deprecated TLS parameter usage. Similar to related work, the fingerprinting achieved a maximum precision of 99 % for a conservative detection threshold of 100 %; and at the same time, we improved the recall by a factor of 2.8.

Commuting by car can be stressful, especially unexpected traffic jams may result in feelings of loss of control and social disconnectedness. In this paper, we present Traeddy, a teddy bear augmented with embedded technology, which serves as a wellbeing companion for car commuters in case of traffic jams. Traeddy is capable to help, for example by notifying relevant contacts about traffic jams and potential delays. We describe in detail the design process, including 20 contextual inquiries and report the evaluation of Traeddy through an online survey with 102 participants and a field study evaluating Traeddy with three commuters and two contacts in the field. The results of the field study indicate that Traeddy has a positive impact on the relationship between the commuter and the notified contact. Furthermore, the majority of the online participants anticipated Traeddy to be useful and to support their wellbeing in traffic jams.