What are the limitations of natural language processing (NLP) in phishing detection?

Natural Language Processing (NLP) has limitations when it comes to detecting sophisticated phishing schemes due to the following reasons:

1. Subtle Linguistic Cues: Sophisticated phishing schemes may involve language that mimics legitimate communication closely, making it challenging for NLP algorithms to differentiate between genuine and fraudulent messages.

2. Contextual Understanding: NLP may struggle to grasp the nuanced context of certain messages, especially in cases where attackers exploit contextual ambiguity to deceive recipients.

3. Evolution of Tactics: Phishing techniques constantly evolve, with attackers employing new tactics and strategies. NLP models may not always be updated or advanced enough to effectively detect these emerging phishing methods.

4. Detection of Visual Elements: NLP primarily focuses on text-based data, so it may not effectively analyze visual elements such as logos, images, or formatting tricks used in phishing emails to deceive recipients.

5. Highly Targeted Attacks: Phishing attacks can be tailored to specific individuals or organizations, incorporating personalized details that make it harder for NLP algorithms to generalize and detect such attacks.

6. Encrypted and Obfuscated Content: Phishing emails often employ encryption or obfuscation techniques to evade detection. NLP algorithms may struggle to analyze such content and uncover malicious intent.

7. False Positives and Negatives: NLP models may produce false positives by incorrectly flagging legitimate messages as phishing attempts or false negatives by missing sophisticated phishing emails, thus posing a challenge in achieving high accuracy rates