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A Deep Learning Approach to Cybersecurity Chatbots: Combining BERT, Cosine Similarity, and Natural Language Processing
Abstract
This study evaluates the comparative performance of Logistic Regression, Naive Bayes, and Support Vector Machines against the paraphrase-distilroberta-base-v1 deep learning model. By encoding textual data into 768 dimensional vector embeddings, the research optimizes semantic search and clustering within complex network environments. The investigation meticulously assesses the “Alice” chatbot, leveraging natural language processing and cosine similarity to navigate intricate frameworks and provide rapid, accurate responses. Crucially, the article examines Alice's efficacy in detecting adversarial attacks and augmenting Security Information and Event Management (SIEM) systems. By analyzing the impact of NLP-driven agents on network monitoring, this research elucidates how such technologies bolster operational efficiency and user engagement. Ultimately, these findings underscore the critical integration of AI-driven chatbots in fortifying defensive postures and streamlining workflows within the cybersecurity and network security sectors.
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