Enhanced Cybersecurity Threat Prediction and Detection in Network Systems Using Extended Convolutional Neural Networks

Cybersecurity threat prediction and detection in network systems entails employing sophisticated methods, like extended Convolutional Neural Networks (CNN), to foresee and recognize potential security violations and malicious actions within a network, preventing damage before it occurs. Implementing extended CNN for cybersecurity threat prediction and detection presents challenges such as the need for substantial computational resources, resulting in high infrastructure costs and possible delays in real-time processing, along with the necessity for high-quality, diverse, and precisely labeled training data to maintain model accuracy and reliability. To overcome these challenges, two extended CNN techniques that can be utilized are transfer learning, which leverages pre-trained models to minimize computational requirements, and data augmentation, which improves the diversity and quality of training data, thereby enhancing model performance and reliability. The proposed system using extended CNN techniques provides the benefits of enhanced accuracy in identifying various cybersecurity threats and lower computational costs by effectively utilizing pre-trained models and data augmentation. After 50 epochs of training, the model reached a test accuracy of 75%, showing consistent improvement from an initial accuracy of 41.38% in the first epoch, despite experiencing fluctuations in validation loss during the training.