分布式网络数据自适应安全优化保护仿真

Simulation of Distributed Network Data Adaptive Security Optimization Protection Simulation

对分布式网络数据进行自适应安全防护,是提高分布式网络安全的重要技术手段。由于分布式网络容易受到差异性入侵的影响,使得分布式网络数据多样,且不完整。传统的网络数据入侵保护方法,主要通过设定阈值判断函数,检测该数据包是否遭到攻击,未考虑到未知漏洞形成的入侵,对数据安全的影响,导致数据报警响应能力差、网络分组投递率低等问题。提出基于粒子群算法的分布式网络数据自适应安全优化保护方法。方法通过分析分布式网络数据已知漏洞的共性,给出漏洞及其采用规则的形式描述,建立数据混合路径,将已知漏洞采用产生的隐含攻击路径以及显示攻击路径表征在相同的数据项中,并计算出数据的漏洞利用率,以计算结果为依据构建数据自适应安全优化保护策略集,意在突出防御代价,为以最小代价阻止分布式网络数据,引入粒子群算法获得最小关键策略集,实现数据自适应安全优化保护。实验结果表明,所提方法可以及时发现数据被入侵行为并做出响应,有效提升了网络数据自适应优化保护。

Traditional method for preventing intrusion of network data does not consider the intrusion formed by unknown vulnerabilities which influences data security. In this paper, a method for optimizing and protecting adaptive security of distributed network data based on particle swarm optimization was proposed. Through analyzing common- ness of known vulnerabilities of distributed network data, this method gave the formal description of loopholes and rules. Then, the method built the mixed path of data. Moreover, the method used the hidden attack path and obvious attack path to characterize known vulnerabilities in the same data item, and calculated the utilization of vulnerability. On the basis of calculation results, we constructed the policy set for optimizing and protecting adaptive security of da- ta, which was meant to highlight the cost of defense and to prevent the distributed network data with the minimum cost. Finally, the particle swarm optimization algorithm was introduced to obtain the minimum policy set and thus to achieve the adaptive optimization and protection for data. Simulation results show that the proposed method can find intrusions quickly and respond in time, which effectively improves the adaptive optimization and protection of network data.