Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data

Abstract

As the reliance on wireless sensor networks (WSNs) rises in numerous sectors, cyberattack prevention and data transmission integrity become essential problems. This study provides a complete framework to handle these difficulties by integrating a cognitive intelligence (CI) framework, an information processing protocol, and sophisticated artificial intelligence (AI) and big data analytics approaches. The CI architecture is intended to improve WSN security by dynamically reacting to an evolving threat scenario. It employs artificial intelligence algorithms to continuously monitor and analyze network behavior, identifying and mitigating any intrusions in real time. Anomaly detection algorithms are also included in the framework to identify packet drop instances caused by attacks or network congestion. To support the CI architecture, an information processing protocol focusing on efficient and secure data transfer within the WSN is introduced. To protect data integrity and prevent unwanted access, this protocol includes encryption and authentication techniques. Furthermore, it enhances the routing process with the use of AI and big data approaches, providing reliable and timely packet delivery. Extensive simulations and tests are carried out to assess the efficiency of the suggested framework. The findings show that it is capable of detecting and preventing several forms of assaults, including as denial-of-service (DoS) attacks, node compromise, and data tampering. Furthermore, the framework is highly resilient to packet drop occurrences, which improves the WSN's overall reliability and performanc