| Abstract |
The primary goal is to strengthen VoIP security by integrating intelligent features: automated speech-to-text conversion for analyzing call content, sentiment analysis to gauge emotional tone, keyword detection to identify suspicious phrases, and behavioral pattern analysis to flag unusual communication styles. By combining these, the system aims to detect phishing and other social engineering attempts as they happen. The system uses a supervised learning approach, trained on a dataset of real and fraudulent calls. This training is crucial for achieving high detection accuracy and minimizing false alarms. It also includes a risk scoring mechanism and generates alerts based on the scores, enabling automated call flagging and real-time notifications to security personnel for immediate intervention.
Our methodology involves several key steps such as collecting data from VoIP logs, preprocessing it for speech analysis, extracting relevant features using NLP techniques, and finally, classifying calls using deep learning models like BERT and LSTM. A user-friendly, web-based dashboard provides real-time monitoring and analytics for security teams to take quick action to mitigate the risk. Our initial findings show the AI-assisted system achieves a promising detection accuracy of 85-95%, significantly reducing false positives through adaptive learning. Deploying this solution within telecom and financial helplines has the potential to significantly reduce fraud, protect sensitive customer data, and ensure compliance with cybersecurity regulations.
In summary, this research demonstrates the potential of AI-driven VoIP security solutions to proactively address social engineering risks. Our system offers an efficient and intelligent defense against evolving cyber threats in the UAE's vital communication sector. Future work will focus on expanding language support and integrating AI-driven call blocking to enable greater defense mechanisms against these cybersecurity crimes.
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