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As the reliance on wireless sensor networks (WSNs) rises in numerous sectors,cyberattack prevention and data transmission integrity become essentialproblems. This study provides a complete framework to handle these difficultiesby integrating a cognitive intelligence (CI) framework, an informationprocessing protocol, and sophisticated artificial intelligence (AI) and bigdata analytics approaches. The CI architecture is intended to improve WSNsecurity by dynamically reacting to an evolving threat scenario. It employsartificial intelligence algorithms to continuously monitor and analyze networkbehavior, identifying and mitigating any intrusions in real time. Anomalydetection algorithms are also included in the framework to identify packet dropinstances caused by attacks or network congestion. To support the CIarchitecture, an information processing protocol focusing on efficient andsecure data transfer within the WSN is introduced. To protect data integrityand prevent unwanted access, this protocol includes encryption andauthentication techniques. Furthermore, it enhances the routing process withthe use of AI and big data approaches, providing reliable and timely packetdelivery. Extensive simulations and tests are carried out to assess theefficiency of the suggested framework. The findings show that it is capable ofdetecting and preventing several forms of assaults, including asdenial-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 performance

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