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

翻译：随着无线传感器网络（WSN）在各行业中的依赖度日益提升，网络攻击防御与数据传输完整性成为关键问题。本研究通过融合认知智能（CI）框架、信息处理协议以及先进的人工智能（AI）与大数据分析方法，提出了一种应对上述挑战的综合性框架。该CI架构旨在通过动态响应不断演变的威胁场景来增强WSN安全性：其运用AI算法持续监测并分析网络行为，实时识别并缓解各类入侵行为；同时框架内置异常检测算法，可识别由攻击或网络拥塞引发的数据包丢失实例。为支撑CI架构，本研究引入了一种聚焦于WSN内部高效安全数据传输的信息处理协议。该协议通过加密与认证技术保障数据完整性并防止未授权访问，同时借助AI与大数据方法优化路由进程，实现可靠及时的数据包投递。通过开展广泛仿真与测试，本研究所提框架的有效性得到验证。结果表明，该框架能够检测并防范包括拒绝服务（DoS）攻击、节点劫持及数据篡改在内的多种攻击类型，且对数据包丢失事件具有高鲁棒性，从而显著提升WSN的整体可靠性与性能。