Biblio

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.

Social media have become increasingly popular for the sharing and spreading of user-generated content due to their easy access, fast dissemination, and low cost. Meanwhile, social media also enable the wide propagation of cyber frauds, which leverage fake information sources to reach an ulterior goal. The prevalence of untrustworthy information sources on social media can have significant negative societal effects. In a trustworthy social media system, trust calculation technology has become a key demand for the identification of information sources. Trust, as one of the most complex concepts in network communities, has multi-criteria properties. However, the existing work only focuses on single trust factor, and does not consider the complexity of trust relationships in social computing completely. In this paper, a multi-criteria trustworthiness calculation mechanism called Info-Trust is proposed for information sources, in which identity-based trust, behavior-based trust, relation-based trust, and feedback-based trust factors are incorporated to present an accuracy-enhanced full view of trustworthiness evaluation of information sources. More importantly, the weights of these factors are dynamically assigned by the ordered weighted averaging and weighted moving average (OWA-WMA) combination algorithm. This mechanism surpasses the limitations of existing approaches in which the weights are assigned subjectively. The experimental results based on the real-world datasets from Sina Weibo demonstrate that the proposed mechanism achieves greater accuracy and adaptability in trustworthiness identification of the network information.

DLR as research organization increasingly faces the task to share its self-developed software with partners or publish openly. Hence, it is very important to harden the softwares to avoid opening attack vectors. Especially since DLR software is typically not developed by software engineering or security experts. In this paper we describe the data-oriented approach of our new found secure software engineering group to improve the software development process towards more secure software. Therefore, we have a look at the automated security evaluation of software as well as the possibilities to capture information about the development process. Our aim is to use our information sources to improve software development processes to produce high quality secure software.

Information on cyber incidents and threats are currently collected and processed with a strong technical focus. Threat and vulnerability information alone are not a solid base for effective, affordable or actionable security advice for decision makers. They need more than a small technical cut of a bigger situational picture to combat and not only to mitigate the cyber threat. We first give a short overview over the related work that can be found in the literature. We found that the approaches mostly analysed “what” has been done, instead of looking more generically beyond the technical aspects for the tactics, techniques and procedures to identify the “how” it was done, by whom and why. We examine then, what information categories and data already exist to answer the question for an adversary's capabilities and objectives. As traditional intelligence tries to serve a better understanding of adversaries' capabilities, actions, and intent, the same is feasible in the cyber space with cyber intelligence. Thus, we identify information sources in the military and civil environment, before we propose to link that traditional information with the technical data for a better situational picture. We give examples of information that can be collected from traditional intelligence for correlation with technical data. Thus, the same intelligence operational picture for the cyber sphere could be developed like the one that is traditionally fed from conventional intelligence disciplines. Finally we propose a way of including intelligence processing in cyber analysis. We finally outline requirements that are key for a successful exchange of information and intelligence between military/civil information providers.