Neural secret key enabled secure cloud storage with efficient packet checker algorithm

Cloud computing technology is utilized for the commercial purpose of implementing virtualization for the storage of data, which is then controlled and made accessible to users via remote servers. With the increased use of various communication devices and direct access to web servers, there is a favorable opportunity to orchestrate attacks and exploit vulnerabilities in a system. Distributed Denial of Service (DDoS) is a commonly used technique employed by attackers in cloud environments. This study introduces a security layer incorporating a Packet Checker Algorithm (PCA) to detect and eliminate counterfeit packets. The algorithm takes into account transmission delay time as well as minimum and maximum thresholds, thereby enhancing the response time of the Intrusion Detection Process (IDP) in a cloud environment that utilizes neural encryption. Neural encryption is used to synchronize neural networks and create new public channel protocols for a secure cloud storage paradigm. This study greatly expands upon the hop count inspection and filtering method. It incorporates the time slot filtering function and implements a unique key set to differentiate between genuine packets and falsified packets. This novel methodology has the capability to detect Distributed Denial of Service (DDoS) attacks and related anomalies during the initial phases of data transmission. The technique considers Time-to-Live (TTL), Hope count, and Transmission-Delay-Time as crucial components.