Advanced Honeypot Architecture for Network Threats Quantifications

In this project, provide a world-class, hybrid monitoring architecture capturing and analyzing new vulnerabilities and exploits as they arise in order to offer an early and scalable warning and analysis of new Internet dangers like worms or automated attacks. This is achieved through the use of low-interaction honeypots as frontline content filters, which increase the exposure of homeopathies with high engagement to these dangers. The host-based methods are captured with the broad coverage of relevant information, such as the packet payload of assaults for rapid detection and assessment. In order to decrease the backend burden, filter prevailing material at network frontends and employ a unique handoff method for network-host interactions. In their used live network measurements over five months as a filtering method to show how successful the predominance of material is. In conjunction with these insights, it shows that hybrid design preserves the details of a given threat while attaining efficiency and scalability. This dissertation will explain how it enables earlier, more confident detection, more thorough forensics, and strong signatures to mitigate risks. In this project, highlight the benefits of this architecture.