Data Analytics Techniques for Privacy Protection in Cybersecurity for Leveraging Machine Learning for Advanced Threat Detection

The research work presents a comprehensive framework integrating data analytics techniques with machine learning for privacy protection in cybersecurity, aimed at enhancing advanced threat detection. The framework's data intake and processing components are Apache Kafka and Apache Spark. TensorFlow and PyTorch are employed to construct models. PySyft comprises the components of the calculation that guarantee privacy. The experimental results indicate that SVMs, RFs, and NNs perform exceptionally well, with accuracy rates of 95%, 96%, and 97%, respectively. The rate of anomaly detection is 85% when using Isolation Forests, and it can reach 95% when using GMM. The average response time is 0.27 seconds, which is achieved through the framework's real-time deployment, which guarantees swift threat identification and response. Organizations can ensure compliance with cybersecurity regulations and safeguard sensitive data by employing privacy-preserving techniques. The proposed method enhances threat detection while protecting sensitive information.