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Zero-Shot Learning in Cybersecurity: A Paradigm Shift in Attack and Defense Strategies

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Advances in Computing and Data Sciences (ICACDS 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2194))

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Abstract

This paper explores the application of zero-shot learning, a novel machine learning approach, in predicting and mitigating cybersecurity threats and defenses. Our research bridges the gap between the theory and practice of using zero-shot learning, a technique that allows models to adapt to unseen scenarios, in anticipating and managing cybersecurity threats. We propose new attack methodologies based on zero-shot learning and discuss the development of robust defense mechanisms. An interdisciplinary approach, combining social engineering, behavioral psychology, and AI governance, underpins our work. Our research also introduces a quantitative model to assess the impact of these emerging threats and a proof-of-concept prototype. In closing, we discuss ethical considerations and advocate for responsible AI practices and robust regulations to prevent misuse. This paper underscores the potential of zero-shot learning to revolutionize cybersecurity practices and preparations for future threats.

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Author information

Authors and Affiliations

  1. College of IST, Pennsylvania State University, University Park, 16803, USA

    Aviral Srivastava

  2. School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India

    Priyansh Sanghavi & Viral Parmar

  3. Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

    Seema Rani

Authors
  1. Aviral Srivastava
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  2. Priyansh Sanghavi
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  3. Viral Parmar
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  4. Seema Rani
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Corresponding author

Correspondence to Priyansh Sanghavi .

Editor information

Editors and Affiliations

  1. G.L. Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India

    Mayank Singh

  2. Academics and Research Department, Jaypee University of Engineering and Technology, Raghogarh, Madhya Pradesh, India

    Vipin Tyagi

  3. School of Computing, Department of Data Science and Engineering, Mohan Babu University, Tirupati, Madhya Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. School of Elect. Engg. & Comp. Science, School of Electrical Engineering and Computer Science, Ottawa, ON, Canada

    Tuncer Ören

  6. University of Paris-Saclay, Paris, France

    Amar Ramdane Cherif

  7. Persistent Systems, Dehradun, Uttarakhand, India

    Ravi Tomar

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Srivastava, A., Sanghavi, P., Parmar, V., Rani, S. (2025). Zero-Shot Learning in Cybersecurity: A Paradigm Shift in Attack and Defense Strategies. In: Singh, M., et al. Advances in Computing and Data Sciences. ICACDS 2024. Communications in Computer and Information Science, vol 2194. Springer, Cham. https://doi.org/10.1007/978-3-031-70906-7_13

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  • DOI: https://doi.org/10.1007/978-3-031-70906-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70905-0

  • Online ISBN: 978-3-031-70906-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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