AI-Powered Threat Intelligence for Cybersecurity: Developing Natural Language Processing Frameworks to Detect Phishing and Text-Based Attacks

Abstract

The rapid evolution of cyber threats has rendered traditional security mechanisms inadequate, particularly against phishing attacks and text-based cyber intrusions. These threats often exploit human vulnerabilities and the complexity of language, crafting deceptive messages that can bypass conventional filters and cause significant damage. The emergence of Natural Language Processing (NLP) within the field of Artificial Intelligence (AI) offers innovative opportunities to address these challenges. By enabling machines to analyze, interpret, and understand human language, NLP provides a powerful tool for detecting malicious intent in textual communications. This paper delves into the development of AI-driven NLP frameworks for identifying phishing schemes and text-based attacks. It highlights the linguistic characteristics of such threats, including deceptive language patterns, urgency-based social engineering tactics, and contextual adaptations to mimic legitimate communications. The study explores key NLP methodologies such as linguistic pattern recognition, semantic analysis, anomaly detection, and sentiment analysis. These approaches allow cybersecurity systems to uncover subtle cues and anomalies that signal potential threats, thus enhancing their detection capabilities. The integration of NLP frameworks into cybersecurity infrastructures presents both opportunities and challenges. While these systems offer significant potential for real-time detection and adaptability, they must contend with adversarial text generation, multilingual content, and the computational demands of large-scale AI models. Furthermore, the scarcity of labeled datasets and the risk of bias in training data pose critical hurdles to the development of robust detection systems.