Natural Language Processing for Cybersecurity: Detecting and Mitigating Social Engineering Attacks
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Abstract
Social engineering attacks remain one of the most effective methods for breaching security systems. This paper explores the application of natural language processing (NLP) to detect and mitigate social engineering threats. By analyzing communication patterns and linguistic features in emails and messages, our NLP model identifies phishing attempts and manipulative language commonly used in social engineering tactics. The study showcases the model’s ability to improve detection rates and reduce false positives, providing organizations with a robust tool for enhancing human and technical defenses against social engineering attacks.
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