Enhancing Cybersecurity and Deepfake Detection with Advanced Techniques

In this paper, we propose an integrated framework that addresses the critical challenges in cybersecurity and deepfake detection by combining advanced machine learning techniques and AI-driven approaches. Our framework includes an enhancement of the Synthetic Minority Oversampling Technique (SMOTE) using the Kalman filter to handle imbalanced datasets in cybersecurity applications, focusing on rare but significant attack patterns. Additionally, we introduce a boundary-based anomaly detection technique utilizing recurrent neural networks (RNNs) for detecting deepfakes, particularly by analyzing facial regions prone to manipulation. We also present a specialized framework for securing Internet of Things (IoT) networks from DODAG Control Message flooding, ensuring robust network operation. Furthermore, we demonstrate the effectiveness of AI-driven lip region analysis for real-time deepfake detection. The integration of these advanced techniques offers a powerful toolkit for addressing both cybersecurity threats and digital media integrity. Experimental results show significant improvements in both detection accuracy and real-time threat prevention.