Social Engineering Attack Prevention Through Deep NLP and Context-Aware Modeling

Abstract

Social engineering attacks are among the most insidious and psychologically driven types of cyberattacks, founded on targeting individuals' trust instead of the technical flaws they exploit. It is sometimes challenging for normal protection mechanisms since normal detection depends so heavily on static rules as well as surface word forms. This paper introduces an effective, smart social engineering attack detection system based on a hybrid deep learning model combining RoBERTa and LSTM networks with an additional context-aware module. The method employs RoBERTa to capture the deep semantic context of text and LSTM to identify sequential patterns in communication. The context-aware module provides value to the model with the addition of external metadata such as sender reputation, organizational identity, communication pattern, urgency indicator and behavior indicators—critical in psychological manipulation identification. It utilizes exhaustive feature engineering to produce linguistic characteristics such as n-grams, POS tags and sentiment along with contextual characteristics such as authority, persuasion strategies and interaction frequency. The model is trained and tested on a hand-gathered dataset of real-world social engineering situations. This study demonstrates the strength of deep NLP unification and contextual awareness for active cybersecurity via a scalable and adaptive approach to defeat advanced social engineering attacks.