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