From Detection to Disruption: Leveraging NLP Insights to Proactively Combat Cybercrime in Bangladesh

Abstract

In an era distinguished by the rising incidence and sophistication of cyber threats, the requirement for effective cybersecurity measures reaches a critical juncture, especially for nations in growth phases such as Bangladesh. To combat cybercrime in Bangladesh, this study explores the use of natural language processing techniques. It places particular emphasis on the integration of natural language processing insights throughout the cybersecurity lifecycle, from detection to disruption. This study begins with a thorough examination of Bangladesh’s present cyber threat landscape, which includes identifying common attack vectors and evaluating the state of the country’s cybersecurity infrastructure. Next, natural language processing is used to improve early threat detection. The objective is to identify significant trends and abnormalities from large amounts of textual data collected from online forums, social media, and other sources. Additionally, the study investigates how incident response mechanisms might use natural language processing-powered tools to help with quick and well-informed decision-making in cyber incidents. The goal is to stop cybercriminals before they cause more harm by adopting a proactive strategy that makes use of natural language processing insights. This will reduce potential harm and increase the cybersecurity ecosystem’s overall resilience. The research includes developing a working prototype system that illustrates how natural language processing can be applied in real-time cyber threat assessments. Simulated cyberattack scenarios will be used to assess the system’s efficacy in detecting and proactively responding to changing threats. In the end, this study hopes to provide insightful analysis and workable solutions that will help law enforcement organizations, cybersecurity experts, and legislators in Bangladesh strengthen their defenses against cyberattacks.