CyberGuard - An AI-Enabled System for Cyberbullying Detection and Real-Time Cybercrime Alerts
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Abstract
Artificial Intelligence (AI) plays a significant role in analyzing large volumes of unstructured data in cybersecurity applications. This paper presents an AI-driven system for detecting cyberbullying and generating automated cybercrime alerts in real time. The proposed system integrates Machine Learning (ML) and Natural Language Processing (NLP) techniques to classify harmful textual content effectively.Text data is preprocessed using tokenization, normalization, stop-word removal, and transformed into numerical features using TF-IDF. Supervised learning algorithms such as Support Vector Machine (SVM) are employed for accurate text classification. The system also supports multilingual input, enabling detection across diverse user interactions.A browser extension is used to capture user-generated content, which is then analyzed by the detection module. Upon identifying abusive or harmful language, the system triggers alerts to notify users or concerned authorities. Additionally, the system leverages modern language models to enhance contextual understanding and improve detection accuracy.This solution aims to provide a scalable and efficient approach to monitor online interactions and mitigate cyberbullying, contributing to safer digital environments.
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