Generative NLP Models for Mitigating Phishing Attacks

The rapid advancement and accessibility of generative Artificial Intelligence have empowered attackers to orchestrate sophisticated social engineering scams, with phishing remaining the primary vector for malicious distribution (Generative NLP Models for Mitigating Phishing Attacks, n.d.). Traditional defense mechanisms, including rule-based systems and conventional machine learning models, often fail to detect these contextually advanced threats as they rely on static patterns and lack the semantic understanding necessary to adapt to dynamic attack behaviors (Generative NLP Models for Mitigating Phishing Attacks, n.d.). Statistics indicate that 40% of AI-generated phishing emails target businesses, and 60% of recipients fall victim to these deceptive messages, a rate comparable to non-AI-generated attacks (Generative NLP Models for Mitigating Phishing Attacks, n.d.). To address these vulnerabilities, this research proposes an adaptive cybersecurity framework leveraging generative Natural Language Processing models, such as BERT and GPT, to identify complex linguistic patterns and contextual cues of malicious intent (Generative NLP Models for Mitigating Phishing Attacks, n.d.). The proposed methodology integrates generalization-based learning with adversarial training to strengthen the system's resilience against evolving and unknown attack strategies (Generative NLP Models for Mitigating Phishing Attacks, n.d.). A key contribution of this work is the development of a model capable of generating synthetic samples for training, thereby reducing the dependency on massive, manually annotated datasets (Generative NLP Models for Mitigating Phishing Attacks, n.d.). By facilitating real-time detection across various channels—including emails, fake websites, and multilingual chat-based conversations—this framework enhances proactive defense mechanisms and significantly reduces the potential for human error in an increasingly intricate digital landscape (Generative NLP Models for Mitigating Phishing Attacks, n.d.)