DeepGuard: A Study on the Use of Deep Learning and NLP in Analyzing Digital Communications to Prevent Social Engineering Attacks

Social engineering is regarded to be one of the most widespread attacks regarding cybersecurity. They are not grounded in a technical weakness of the system and unlawfully access sensitive information but on a human psyche. The conservative rule of thumb detection system cannot stand in any place to decide on the peculiarities of the language or of the Engineering gimmicks of internet communication, with a great ease. The resultant effect is poor detection rates and great organization risk. The software is called DeepGuard and it is deployed to process electronic messages, email, and instant message using a deep learning model and natural language processing (NLP). It is aimed at detecting fraud and evil intentions. It was a benchmark based quantitative study. The data, that are pertinent to the present project, are the Enron Email Dataset and the Social Engineering Attack Dataset (SEAD). They have been trained and used as a testing data with a wide range of deep learning models: transformer, constituent neural network (CNNs), and recurrent neural network (RNNs). The evaluation values of the accuracy, and Precision, recall and F1-score were equated. The results indicated that transformer based model was rather effective as compared to other models. This is because of the fact that they are able to learn complex cues and dependencies of language. They, in turn, would be in a position to measure low false-negatives and enhanced early detection. The proposed system is also applicable at the enterprise level of the security solutions since it is scalable and flexible enough to support data in the real world communication. It could be demonstrated in the paper that such tools as deep learning and natural language processing (NLP) can be applied to optimize the process of social engineering attacks detection. It also provides suggestions on the research that should be carried out in future which encompass adaptive learning and real time monitoring.