神经网络在数字化校园安全检测中的应用

数据安全是数字化校园建设的重要问题,快速准确检测数字化校园的安全性及存在的风险和漏洞,成为急需解决的问题.在改进BP算法基础上,设计一个基于神经网络的数字化校园安全检测原型.通过统计底层网络协议(TCP)的数据流量和信息数据包协议头的信息,将信息预处理后送入已训练过的神经网络模块,以此判断当前网络数据流量存在的攻击或扫描行为.实现快速检测数字化校园存在的漏洞和安全隐患,提前预防和减少数字化校园受到的攻击和破坏.

Application of Neural Network in the Digital Campus Safety Testing

Data security is an important problem in digital campus construction. It is urgent to find a way to detect the risk and vulnerabilities of digital campus security. Therefore, a digital campus etwork security detection prototype was designed based on the improved BP algorithm. The data flow and nformation coming from the underlying network protocols(TCP) will be collected and pre-handled. The trained neural network module will react to these data and information, which gives the clue to determine the existing attack or scanning behavior in current network data flow. Rapid detection of digital campus existing vulnerabilities and security risks can prevent and reduce the attack and destruction.