Node Level Tracking for a Self-Defending Network

The field of cyber security is constantly evolving as new threats emerge, and traditional security measures often are reactive in nature, leading to networks being vulnerable to sophisticated attacks. A self-defending and anticipatory network aims to address this problem by proactively identifying potential threats, anticipating attacks, and automatically responding to them in real time. In this paper, the authors suggest a new kind of decentralized architecture for a self-defending network wherein each node in the network maintains a data structure called Failure Probability Table or FPT to keep track of other nodes behavior. The value in the FPT for each node will be decided on the basis of real time machine learning algorithms. The FPT values help in isolating the node, and also pre-emptive rerouting to minimize the effects on the network based on certain thresholds. This novel approach of each node having its own FPT values has the potential to substantially enhance the robustness of network security, effectively countering sophisticated attacks and fortifying against evolving threats in a proactive manner.