Because of the low accuracy in intrusion detection, a model of extreme learning machine based on the optimization of quantum beetle swarm algorithm is proposed. First of all, this paper proposes a quantum beetle swarm optimization algorithm, which introduces quantum mechanics and combines the advantages of beetle antennae search and particle swarm optimization. In this way, the individual can learn both their own experience and group experience, which enables the beetle to move purposefully and instructively, and improves the convergence performance of the algorithm. In extreme learning machine, it is more difficult to solve the problem in high-dimensional data. This paper proposed an improved extreme learning machine that uses the least squares QR algorithm to decompose the matrix, which can reduce the computational complexity of the traditional extreme learning machine. The improved extreme learning machine model optimized by quantum beetle swarm optimization algorithm is applied to intrusion detection, and the simulation results show that the model proposed in this paper can significantly improve detection accuracy and increase convergence rate.

针对入侵检测精度不高的问题，提出了一种基于量子甲虫群算法优化的极限学习机模型。首先，本文提出了一种量子甲虫群优化算法，该算法引入了量子力学，结合了甲虫触角搜索和粒子群优化的优点。这样，个体既可以学习自己的经验，也可以学习群体的经验，使甲虫有目的地、有指导意义地移动，提高算法的收敛性能。在极限学习机中，解决高维数据中的问题比较困难。本文提出了一种改进的极限学习机，采用最小二乘QR算法对矩阵进行分解，可以降低传统极限学习机的计算复杂度。将采用量子甲虫群优化算法优化的改进型极限学习机模型应用于入侵检测，仿真结果表明，本文提出的模型能够显着提高检测精度，提高收敛速度。