Campus Network Security Research based on Random Forest Algorithm

With the continuous development of network technology, the campus network construction of colleges and universities has experienced three stages from the traditional campus network, electronic campus network to digital campus network, and is now moving forward from digital campus to smart campus. In the current era of environment, the campus network structure is relatively simple, development is not mature, on the network information is uneven, complicated information full of cyberspace, thing, college students long exposure in the network environment is easy to be instilled by bad information, and the students themselves for the network information recognition ability is not strong, Weak awareness of prevention, these provide opportunities for hacking, students use campus network security can not be guaranteed. How to improve the security of campus networks, and eliminate the risk of campus network security, so that college students can safely and effectively use the network system, is an important topic that we urgently need to study. Cyber-attacks in recent years, increasingly complicated, intrusion detection technology is one of the effective ways to prevent malicious attacks, by random forest algorithm to predict abnormal network data flow, the technology based on data stream classification and prediction of abnormal network data flow, timely discover and deal with the network information security problems appeared in the process of informatization, Enhance the awareness of network security of college students, to avoid the purpose of network attacks, to create a positive and healthy network environment for college students.