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Developing Chatbots for Cyber Security: Assessing Threats through Sentiment Analysis on Social Media

Author

Listed:
  • Amit Arora

    (School of Business and Public Administration, University of the District of Columbia, Washington, DC 20008, USA)

  • Anshu Arora

    (School of Business and Public Administration, University of the District of Columbia, Washington, DC 20008, USA)

  • John McIntyre

    (Scheller College of Business, Georgia Institute of Technology, Atlanta, GA 30332, USA)

Abstract

In recent years, groups of cyber criminals/hackers have carried out cyber-attacks using various tactics with the goal of destabilizing web services in a specific context for which they are motivated. Predicting these attacks is a critical task that assists in determining what actions should be taken to mitigate the effects of such attacks and to prevent them in the future. Although there are programs to detect security concerns on the internet, there is currently no system that can anticipate or foretell whether the attacks will be successful. This research aims to develop sustainable strategies to reduce threats, vulnerability, and data manipulation of chatbots, consequently improving cyber security. To achieve this goal, we develop a conversational chatbot, an application that uses artificial intelligence (AI) to communicate, and deploy it on social media sites (e.g., Twitter) for cyber security purposes. Chatbots have the capacity to consume large amounts of information and give an appropriate response in an efficient and timely manner, thus rendering them useful in predicting threats emanating from social media. The research utilizes sentiment analysis strategy by employing chatbots on Twitter (and analyzing Twitter data) for predicting future threats and cyber-attacks. The strategy is based on a daily collection of tweets from two types of users: those who use the platform to voice their opinions on important and relevant subjects, and those who use it to share information on cyber security attacks. The research provides tools and strategies for developing chatbots that can be used for assessing cyber threats on social media through sentiment analysis leading to a global sustainable development of businesses. Future research may utilize and improvise on the tools and strategies suggested in our research to strengthen the knowledge domain of chatbots, cyber security, and social media.

Suggested Citation

  • Amit Arora & Anshu Arora & John McIntyre, 2023. "Developing Chatbots for Cyber Security: Assessing Threats through Sentiment Analysis on Social Media," Sustainability, MDPI, vol. 15(17), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13178-:d:1231337
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    References listed on IDEAS

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    1. Fellnhofer, Katharina, 2023. "Positivity and higher alertness levels facilitate discovery: Longitudinal sentiment analysis of emotions on Twitter," Technovation, Elsevier, vol. 122(C).
    2. Sheehan, Ben & Jin, Hyun Seung & Gottlieb, Udo, 2020. "Customer service chatbots: Anthropomorphism and adoption," Journal of Business Research, Elsevier, vol. 115(C), pages 14-24.
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    Cited by:

    1. S. M. F. D. Syed Mustapha & Edmund Evangelista & Farhi Marir, 2023. "Towards Designing a Knowledge Sharing System for Higher Learning Institutions in the UAE Based on the Social Feature Framework," Sustainability, MDPI, vol. 15(22), pages 1-23, November.

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