Revocation of digital certificates represents a series of improvements by IETF in order to standardize a complete and effective solution. This applies to the context of Internet web sites in which web servers and browsers use digital certificates to establish Transport Layer Security (TLS). Despite IETF’s effort over the years to establish a reliable revocation mechanism, including Certificate Revocation List (CRL), Online Certificate Status Protocol (OCSP) and its variants, various technical issues hinder complete resolution of the revocation problem. At the same time, all major browser vendors implement their own proprietary solutions to address the revocation problem. As a result, revocation solutions are fragmented, incomplete, and ineffective, and the level of real-world acceptance of standardized solutions is limited. To address this situation, in 2020, IETF has introduced short-term certificate concept to avoid revocation altogether. It is called Support for Short-Term, Automatically Renewed (STAR) which recommends a validity period of 4 days. To measure the level of adoption of this new approach in the Internet, we collected and analyzed web server certificates from 1 million websites; the result of our extensive analysis indicates that this scheme has not gained traction in reality. In fact, we found no implementation of a 4-day validity period out of more than 1.5 million server certificates that we collected. This situation indicates that the latest IETF effort to promote short-term certificates has not materialized, with no clear alternative solution in sight to resolve the revocation issue. We present our insights into the reasons for this absence of traction in reality and present our view of a possible way forward.

Assessing the overall accuracy of deep learning classifier is not a sufficient criterion to argue for safety of classification based functions in highly automated driving. The causes of deviation from the intended functionality must also be rigorously assessed. In context of functions related to image classification, one of the causes can be the failure to take into account during implementation the classifier's vulnerability to misclassification due to high similarity between the target classes. In this paper, we emphasize that while developing the safety assurance case for such functions, the argumentation over the appropriate implementation of the functionality must also address the vulnerability to misclassification due to class similarities. Using the traffic sign classification function as our case study, we propose to aid the development of its argumentation by: (a) conducting a systematic investigation of the similarity between the target classes, (b) assigning a corresponding classifier vulnerability rating to every possible misclassification, and (c) ensuring that the claims against the misclassifications that induce higher risk (scored on the basis of vulnerability and severity) are supported with more compelling sub-goals and evidences as compared to the claims against misclassifications that induce lower risk.