연구 분야: Safety
학회: IBIMA Conference on Artificial intelligence and Machine Learning
The main goal of this study is finding optimal solutions for understand and assess security risks and to implement controls. The effectiveness of risk management tools in securing these networks is paramount to maintaining the strategic advantage and safeguarding critical assets. This study aims to evaluate and enhance the tools used for risk management in the context of local military network security. The motive behind the study is adapting to technological advancements, like ensuring that the tools and practices used comply with military standards and regulations related to cybersecurity. The study employs a mixed-methods research design, combining both quantitative and qualitative approaches to gain a comprehensive understanding of the effectiveness of risk management tools in securing local military networks. This approach ensures that both statistical data and expert insights are considered. The voids identified in the existing literature underscore the importance of this study. By focusing on the specific requirements and challenges of local military networks, integrating advanced technologies, evaluating operational impacts, and providing real-world case studies, this research aims to fill critical gaps. The study’s findings will contribute to a more comprehensive understanding of the effectiveness of risk management tools in military contexts, ultimately enhancing the security and resilience of these vital networks. The study finds that while current risk management tools are generally effective in securing local military networks, there are areas that require improvement. Advanced technologies like AI, ML, and big data analytics show great promise but need better integration and scalability.
| 발행 연도 | 2025년 |
|---|---|
| 인용수 | 0 |
| 출판 국가 | |
| 사이트 | Springer |
| 좋아요 수 | 0 |