Variational Autoencoders using Convolutional neural network for highly advanced cyber threats
Anita priyadarshini Durai pandian · 2024
In the ever-evolving landscape of cybersecurity, the detection of highly advanced cyber threats demands innovative approaches. This abstract introduces a novel framework that harnesses the power of Variational Autoencoders (VAEs) and Convolutional Neural Networks (CNNs) for advanced cyber threat detection. The proposed system is designed to learn, model, and uncover subtle anomalies within network traffic data, enabling the identification of complex and sophisticated cyberattacks. In this framework, a VAE is trained to learn a probabilistic latent representation of network data, allowing it to capture essential features and structures inherent in both normal and malicious traffic. The VAE serves as the foundation for a subsequent CNN-based anomaly detection model, which utilizes the latent representations to identify patterns, outliers, and cyber threats. By integrating these two neural network architectures, the system leverages the VAE's data compression and representation learning capabilities, while the CNN excels in pattern recognition and anomaly detection. The deployment of this advanced system within network infrastructure empowers organizations to continuously monitor and adapt to emerging threats. Its performance is evaluated using established metrics, ensuring the detection of cyber threats with high precision and recall. Additionally, the model can be integrated with external threat intelligence sources to enhance its threat detection capabilities. The VAE-CNN framework presents a promising approach for addressing the ever-increasing complexity of cyber threats, providing an adaptable, intelligent, and data-driven solution for the protection of critical digital assets and network infrastructure. Its ability to uncover highly advanced cyber threats has the potential to significantly enhance the security posture of organizations in the face of evolving cybersecurity challenges.