TECHNOLOGY AREA(S): Human Systems, Information Systems

ACQUISITION PROGRAM: Supports approved FY20 Tech Candidate: Cyber Information Environment for Assessment Nexus (CIEAN)

OBJECTIVE: Develop a capability to detect suspected "bot" (artificial) accounts, using a probabilistic model that should have a greater than 60% accuracy in detecting bots or bot-assisted accounts in Twitter. Desired qualities include ease of use, ability to monitor or flag suspected bots, and to identify, block, or unfollow suspect accounts with explanations available to help the user better understand the bots that they may interact with on Twitter.

DESCRIPTION: This STTR topic seeks development of a web-based tool or app that can be used to assess an account�s followership and friends to indicate which of these accounts are likely to be bots or bot-assisted. The tool would require the user to input their credentials; and would then scan the followers and friends for signs that these accounts were bots or bot-assisted according to an internal, proprietary model. The tool would provide situation awareness that will enable users to unfollow or block bots easily or to monitor a bot for a short period to assess its activities. The bot detection model should have an accuracy of greater than 60%. The tool needs to have the capability to assess the threat level of the dormant bots. Once identified, the tool should have the ability to unfollow the bots in batches and to block bots, if the user desires.

PHASE I: Develop and/or improve algorithms for detecting bots in Twitter to develop a model for bot detection, ideally improving or adapting an existing model or set of algorithms. Create a system for acquiring the data on followers and friends from a user�s Twitter account and create a simple web-based prototype, suitable for testing and validation. Develop a Phase II plan.

PHASE II: Create a mobile application and a web-based tool from the prototype. Ensure that model results would be exportable to other tools. Develop a user-friendly interface available for testing and evaluation. Include highly desirable built-in help features and guidance capabilities. Develop additional requirements for Phase III through engagement with stakeholders and potential customers.

PHASE III DUAL USE APPLICATIONS: Make the technology available on My Navy Portal. Expansion and development of models and capabilities, including functions to create a database of dormant bots, interoperable with other tools, is desirable. Capabilities to manage the database and deal with the needs of multiple customers would be developed. Both web-based and app tools would be of great utility to corporations, agencies, and individual users.

REFERENCES:

1. @DFRab �#Botspot: Twelve Ways to Spot a Bot.� Medium blog, 28 August 2017. https://medium.com/dfrlab/botspot-twelve-ways-to-spot-a-bot-aedc7d9c110c

2. Beskow, David M. and Carley, Kathleen.� �Social Cybersecurity: An Emerging National Security Requirement.�� English Military Review, March-April 2019.� https://www.armyupress.army.mil/Portals/7/military-review/Archives/English/MA-2019/Beskow-Carley-Social-Cyber.pdf

3. Brundage, Mike, et. al.� �The Malicious Use of Artificial Intelligence: Forecasting, Prevention and Mitigation.��� Future of Humanity Institute, University of Oxford: Arizona State University, February 2018.� https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf

4. Lapowsky, Issie. �NATO Group Catfished Soldiers to Prove a Point about Accuracy.�� Wired. https://www.wired.com/story/nato-stratcom-catfished-soldiers-social-media/

5. Nimmo, Ben, Czuperski, Maks and Brookie. Graham.� �#BotSpot: The Intimidators.�� @DFRLab blog. https://medium.com/dfrlab/botspot-the-intimidators-135244bfe46b

6. Schreckinger, Ben. �How Russia Targets the U.S. Military.�� Politico, June 12, 2017. https://www.politico.com/magazine/story/2017/06/12/how-russia-targets-the-us-military-215247

KEYWORDS: C4ISR; Cyber Terrorism; Hybrid; Cyborg; Smart Botnets; Information Operations; Defensive Communication