An Efficient Cyber Security Attack Detection With Encryption Using Capsule Convolutional Polymorphic Graph Attention

Abstract

As digitalization permeates all aspects of life, the Internet has become a critical platform for communication across various domains. Workstations within organizations often handle sensitive and private data, underscoring the need for encryption to safeguard information and prevent unauthorized access. Despite advances in system security, challenges remain in the form of system vulnerabilities and evolving cyber threats. Intrusion detection using deep learning (DL), which serves as the second line of defense after firewalls, has progressed rapidly, yet still faces issues such as misclassification, false positives, and delayed or inadequate responses to attacks. These ongoing problems necessitate continuous improvement in system security screening and intrusion detection to protect networks effectively. Therefore, in this research, a novel DL framework called capsule convolutional polymorphic graph attention neural network with tyrannosaurus optimization algorithm (CCPGANN-TOA) is utilized for attack detection due to its advanced feature representation, graph attention for focusing on key data points, polymorphic graphs for adaptability, and TOA for performance optimization. Normal data are then encrypted using the digital signature algorithm based on elliptic curve cryptography (DSA-ECC) because it provides strong security with smaller key sizes, resulting in faster computations and efficient resource utilization. The proposed method outperforms traditional approaches in terms of 99.98% accuracy of data set I, 99.9% accuracy of data set II, and 900 Kbps higher throughput with low delay.