The Future of Cyber-Enabled Influence Operations: Emergent Technologies, Disinformation, and the Destruction of Democracy

Nation-states have been embracing online influence campaigns through disinformation at breakneck speeds. Countries such as China and Russia have completely revamped their military doctrine to information-first platforms [1, 2] (Mattis, Peter. (2018). China’s Three Warfares in Perspective. War on the Rocks. Special Series: Ministry of Truth. https://warontherocks.com/2018/01/chinas-three-warfares-perspective/, Cunningham, C. (2020). A Russian Federation Information Warfare Primer. Then Henry M. Jackson School of International Studies. Washington University. https://jsis.washington.edu/news/a-russian-federation-information-war fare-primer/.) to compete with the United States and the West. The Chinese principle of “Three Warfares” and Russian Hybrid Warfare have been used and tested across the spectrum of operations ranging from competition to active conflict. With the COVID19 pandemic limiting most means of face-to-face interpersonal communi-cation, many other nations have transitioned to online tools to influence audiences both domestically and abroad [3] (Strick, B. (2020). COVID-19 Disinformation: Attempted Influence in Disguise. Australian Strategic Policy Institute. International Cyber Policy Center. https://www.aspi.org.au/report/covid-19-disinformation.) to create favorable environments for their geopolitical goals and national objectives. This chapter focuses on the landscape that allows nations like China and Russia to attack democratic institutions and discourse within the United States, the strategies and tactics employed in these campaigns, and the emergent technologies that will enable these nations to gain an advantage with key populations within their spheres of influence or to create a disadvantage to their competitors within their spheres of influence. Advancements in machine learning through generative adversarial networks [4] (Creswell, A; White, T; Dumoulin, V; Arulkumaran, K; Sengupta, B; Bharath, A. (2017) Generative Adversarial Networks: An Overview. IEE-SPM. April 2017. https://arxiv.org/pdf/1710.07035.pdf.) that create deepfakes [5] (Whit-taker, L; Letheren, K; Mulcahy, R. (2021). The Rise of Deepfakes: A Conceptual J. Littell envelope symbolenvelope symbolenvelope symbol Army Cyber Institute at the West Point, United States Military Academy, West Point, NY 10996, USA e-mail: joseph.littell@westpoint.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A.Farhadietal. (eds.), The Great Power Competition Volume 3, https://doi.org/10.1007/978-3-031-04586-8_10 197 198 J. Littell Framework and Research Agenda for Marketing. https://journals.sagepub.com/doi/ abs/10.1177/1839334921999479.) and attention-based transformers [6](https:// arxiv.org/abs/1810.04805.) (Devlin et al., 2018) that create realistic speech patterns and interaction will continue to plague online discussion and information spread, attempting to cause further partisan divisions and decline of U.S. stature on the world stage and democracy as a whole.https://digitalcommons.usmalibrary.org/aci_books/1020/thumbnail.jp