Biblio

Filters: Author is Straub, Jeremy  [Clear All Filters]
2020-07-03
Straub, Jeremy.  2019.  Cyber Mutual Assured Destruction as a System of Systems and the Implications for System Design. 2019 14th Annual Conference System of Systems Engineering (SoSE). :137—139.

Mutual assured destruction is a Cold War era principle of deterrence through causing your enemy to fear that you can destroy them to at least the same extent that they can destroy you. It is based on the threat of retaliation and requires systems that can either be triggered after an enemy attack is launched and before the destructive capability is destroyed or systems that can survive an initial attack and be launched in response. During the Cold War, the weapons of mutual assured destructions were nuclear. However, with the incredible reliance on computers for everything from power generation control to banking to agriculture logistics, a cyber attack mutual assured destruction scenario is plausible. This paper presents this concept and considers the deterrent need, to prevent such a crippling attack from ever being launched, from a system of systems perspective.

2020-08-28
Traylor, Terry, Straub, Jeremy, Gurmeet, Snell, Nicholas.  2019.  Classifying Fake News Articles Using Natural Language Processing to Identify In-Article Attribution as a Supervised Learning Estimator. 2019 IEEE 13th International Conference on Semantic Computing (ICSC). :445—449.

Intentionally deceptive content presented under the guise of legitimate journalism is a worldwide information accuracy and integrity problem that affects opinion forming, decision making, and voting patterns. Most so-called `fake news' is initially distributed over social media conduits like Facebook and Twitter and later finds its way onto mainstream media platforms such as traditional television and radio news. The fake news stories that are initially seeded over social media platforms share key linguistic characteristics such as making excessive use of unsubstantiated hyperbole and non-attributed quoted content. In this paper, the results of a fake news identification study that documents the performance of a fake news classifier are presented. The Textblob, Natural Language, and SciPy Toolkits were used to develop a novel fake news detector that uses quoted attribution in a Bayesian machine learning system as a key feature to estimate the likelihood that a news article is fake. The resultant process precision is 63.333% effective at assessing the likelihood that an article with quotes is fake. This process is called influence mining and this novel technique is presented as a method that can be used to enable fake news and even propaganda detection. In this paper, the research process, technical analysis, technical linguistics work, and classifier performance and results are presented. The paper concludes with a discussion of how the current system will evolve into an influence mining system.