基于改进粒子群优化和极限学习机的网络安全态势预测

针对网络安全态势预测模型预测精度不高、收敛较慢等问题，提出了一种基于改进粒子群优化极限学习机（IPSO-ELM）算法的预测方法。首先，通过改进粒子群优化（PSO）算法中的惯性权重和学习因子来实现两种参数随着迭代次数增加的自适应调整，使PSO初期搜索范围大、速度高，后期收敛能力强、稳定。其次，针对PSO易陷入局部最优的问题，提出一种粒子停滞扰动策略，将陷入局部最优的粒子重新引导至全局最优飞行。改进粒子群优化（IPSO）算法既保证了全局寻优的能力，又对局部搜索能力有所增强。最后，将IPSO与极限学习机（ELM）结合来优化ELM的初始权值及阈值。与ELM相比，结合IPSO的ELM的预测精度提高了44.25%。实验结果表明，与PSO-ELM相比，IPSO-ELM的预测结果拟合度可达到0.99，收敛速度提升了47.43%。所提算法在预测精度和收敛速度等指标上明显优于对比算法。

Network security situation prediction based on improved particle swarm optimization and extreme learning machine

Focusing on the problems of low prediction accuracy and slow convergence speed of network security situation prediction model, a prediction method based on Improved Particle Swarm Optimization Extreme Learning Machine (IPSO-ELM) algorithm was proposed. Firstly, the inertia weight and learning factor of Particle Swarm Optimization (PSO) algorithm were improved to realize the adaptive adjustment of the two parameters with the increase of iteration times, so that PSO had a large search range and fast speed at the initial stage, strong convergence ability and stability at the later stage. Secondly, aiming at the problem that PSO is easy to fall into the local optimum, a particle stagnation disturbance strategy was proposed to re-guide the particles trapped in the local optimum to the global optimal flying. The Improved Particle Swarm Optimization (IPSO) algorithm obtained in this way ensured the global optimization ability and enhanced the local search ability. Finally, IPSO was combined with Extreme Learning Machine (ELM) to optimize the initial weights and thresholds of ELM. Compared with ELM, the ELM combining with IPSO had the prediction accuracy improved by 44.25%. Experimental results show that, compared with PSO-ELM, IPSO-ELM has the fitting degree of prediction results reached 0.99, and the convergence rate increased by 47.43%. The proposed algorithm is obviously better than the comparison algorithms in the prediction accuracy and convergence speed.