International Symposium on Telecommunication (IST)

Home / 11th International Symposium on Telecommunication (IST'2024)

Large Language Model (LLM) for Estimating the Cost of Cyber-attacks

Authors :

Hooman Razavi¹ Mohammad Reza Jamali²

1- Tecnologico de Monterrey 2- Pulseware Co.

Keywords :

Large Language Models،Deep Learning،Big Data Analytics،QoS،Statistical Analysis،Cyber-Attack،Cyber Risk Assessment

Abstract :

With the expansion of digital services and intelligent agents, cyber-attacks are increasingly frequent and impactful. Estimating the financial consequences of these attacks has become crucial in guiding investments in mitigation and defense strategies. This paper proposes a framework utilizing Large Language Models (LLMs) and big data analytics to estimate the financial costs of cyber threats, focusing on lost business opportunities. The case study explores the banking industry, a frequent target of cyber-attacks that result in substantial financial losses and undermine customer trust. By analyzing more than 23 billion transactions, the LLM algorithm identifies demand patterns of bank business activities and calculates losses during downtimes. The results compare the performance of LLMs with Deep Learning, Support Vector Machines (SVM), and Random Walk in terms of accuracy in estimating business activity patterns and illustrate that LLMs outperformed the alternatives. The findings introduce a methodology for calculating the cost of cyber-attacks specific to the banking sector, which can also be adapted for other services. The study highlights the significant costs incurred per hour of an attack, emphasizing the critical need for robust cybersecurity measures and effective risk mitigation strategies.