Generating Voice Text of Cyber Crime in Explainable AI Using Large Language Model
Keywords:
Cyber Crime, Explainable Artificial Intelligence, Generative Voice Text, Large Language ModelAbstract
Machine learning is an AI application that mimics human intelligence's ability to learn from experience, particularly in pattern recognition. This is crucial in criminal justice, as AI aims to replicate human capabilities in software algorithms and hardware, such as identifying people, performing complex tasks, and making predictions. Data leaks are increasing due to work-life balance changes and remote working, with workers accounting for 22% and attackers 23%. Cybercriminals use social engineering techniques like phishing to trick employees into providing sensitive information. Regular cybersecurity awareness training and fostering a positive workplace culture are essential for preventing data leaks. This study examines how cybercriminals impersonate real-world emails, voice and IT help desks in order to fool workers into disclosing critical information. They do this by using phishing and one-on-one social engineering techniques. Through the use of a large language model to generate speech text for electronic crime using explainable artificial intelligence. The study demonstrated strong predictive performance with an imbalanced cybercrime dataset, but identified limitations in the ROC AUC metric, which compares True Positive Rate to False Positive Rate.
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A.S. Rajawat, R. Rawat, R.N. Shaw and A. Ghosh: Cyber Physical System Fraud Analysis by Mobile Robot in Machine Learning for Robotics Applications. Studies in Computational Intelligence, Singapore: Springer, vol. 960, 2021.
B. Rajput: Exploring the Phenomenon of Cyber Economic Crime in Cyber Economic Crime in India, Springer, pp. 53-78, 2020.
Christopher Rigano: Using Artificial Intelligence to Address Criminal Justice Needs National Institute of Justice | NIJ.ojp.gov, NIJ Journal / Issue No. 280, January 2019.
Fawcett, T: An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874,2006.
Gunay Abdiyeva-Aliyeva, Jeyhun Aliyev, Ulfat Sadigov: Application of classification algorithms of Machine learning in cybersecurity, https://doi.org/10.1016/j.procs.2022.12.093. Procedia Computer Science, Volume 215, Pages 909-919,2022.
J. Jang-Jaccard and S. Nepal: A survey of emerging threats in cybersecurity, Journal of Computer and System Sciences, vol. 80, no. 5, pp. 973-993, 2014.
K. Veena, K. Meena, Ramya Kuppusamy, Yuvaraja Teekaraman, Ravi V. Angadi, Amruth Ramesh Thelkar: Cybercrime: Identification and Prediction Using Machine Learning Techniques, Compute Intelligence Neuroscience, 2022; 2022: 8237421, Published online 2022 Aug 27, Doi:10.1155 /2022 /8237,2022.
Lisa Quest, Anthony Charrie, and Subas Roy: The Risks and Benefits of Using AI to Detect Crime, Oliver Wyman Risk Journal Volume 8, Harvard Business Review on August 9, 2018.
M. Kadoguchi, H. Kobayashi, S. Hayashi, A. Otsuka and M. Hashimoto: Deep Self-Supervised Clustering of the Dark Web for Cyber Threat Intelligence, 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 1-6, November 2020.421,2020.
Provost, F., & Fawcett, T: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, Inc,2013.
R. Rawat, V. Mahor, S. Chirgaiya, R.N. Shaw and A. Ghosh: Sentiment Analysis at Online Social Network for Cyber-Malicious Post Reviews Using Machine Learning Techniques in Computationally Intelligent Systems and their Applications, Studies in Computational Intelligence, Singapore: Springer, vol. 950, 2021.
R. Rawat, A.S. Rajawat, V. Mahor, R.N. Shaw and A. Ghosh: Surveillance Robot in Cyber Intelligence for Vulnerability Detection in Machine Learning for Robotics Applications, Studies in Computational Intelligence, Singapore: Springer, vol. 960, 2021.
S. Kaur and S. Randhawa: Dark Web: A Web of Crimes, Wireless Personal Communications, vol. 112, no. 4, pp. 2131-2158, 2020.
Vinod Mahor; Romil Rawat; Shrikant Telang; Bhagwati Garg; Debajyoti Mukhopadhyay; Prajyot Pali: Machine Learning based Detection of Cyber Crime Hub Analysis using Twitter Data, 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Date of Conference: 24-26 September 2021, Date Added to IEEE Xplore: 02 November 2021.
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Copyright (c) 2024 C. Syamsundar Reddy, G. Anjan Babu
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