人工智能视域下的互联网数据安全优化算法研究

Research on Internet Data Security Optimization Algorithm from the Perspective of Artificial Intelligence

网络安全态势会直接影响互联网的发展,为网络系统安全、稳定地运行奠定基础。文中通过转化RBF神经网络权值,并形成粒子群中粒子,让粒子在解空间内部寻找全局最优权值,同时需将最优解问题的解的空间维度优化成五维,若将5个优化问题的最优化解与PSO算法相对应,对RBF神经网络的权重进行编码。结果显示,与RBF神经网络相比,粒子群优化RBF神经网络预测误差的起伏更小。如果训练频率较低,该算法能实现较快的预测,且预测结果接近于互联网安全态势的真实值,实现更快、更有效的预测。

The cyber security situation will directly affect the development of the Internet and lay the foundation for the safe and stable operation of the network system.In this paper,the weights of RBF neural networks are transformed to form particles in the particle swarm.The particles find the global optimal weights in the solution space.At the same time,the spatial dimension of the solution of the optimal solution problem needs to be optimized into five dimensions.If the optimal solution of the five optimization problems corresponds to the PSO algorithm,the weights of RBF neural networks can be encoded.The results show that compared with RBF neural networks,the prediction error of particle swarm optimization RBF neural networks fluctuates less.If the training frequency is low,the algorithm can achieve faster predictions,and the prediction results are close to the true value of the Internet security situation,achieving faster and more effective predictions.