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Many cyber-physical systems (CPS) are critical infrastructure. Security attacks on these critical systems can have catastrophic consequences, putting human lives at risk. Consequently, it is very important to pace CPS systems to red-teaming/blue teaming exercises to understand vulnerabilities and the progression/impact of cyber attacks on them. Since it is not always prudent to conduct such security exercises on live CPS, researchers use CPS testbeds to conduct security-related experiments. Often, such testbeds are very expensive. Since attack scripts used in red-teaming/blue-teaming exercises are, in the strictest sense of the term, malicious in nature, there is a need to protect the testbed itself from these attack experiments that have the potential to go awry. Moreover, when multiple experiments are conducted on the same testbed, there is a need to maintain isolation among these experiments so that no experiment can accidentally or maliciously affect/compromise others. In this work, we describe a novel security architecture and framework to ensure protection of security-related experiments on a CPS testbed and at the same time support secure communication services among simultaneously running experiments based on well-formulated access control policies.

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking – a significant filter, given the sheermore »volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news, and are hence misleading.« less

Organizations collect data from various sources, and these datasets may have characteristics that are unknown. Selecting the appropriate statistical and machine learning algorithm for data analytical purposes benefits from understanding these characteristics, such as if it contains temporal attributes or not. This paper presents a theoretical basis for automatically determining the presence of temporal data in a dataset given no prior knowledge about its attributes. We use a method to classify an attribute as temporal, non-temporal, or hidden temporal. A hidden (grouping) temporal attribute can only be treated as temporal if its values are categorized in groups. Our method uses a Ljung-Box test for autocorrelation as well as a set of metrics we proposed based on the classification statistics. Our approach detects all temporal and hidden temporal attributes in 15 datasets from various domains.