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Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm

机译：改进的人工免疫算法优化的基于通用回归神经网络的网络入侵检测

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To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data.
机译：为了有效，准确地检测和分类网络入侵数据，本文介绍了一种基于精英免疫算法（AIAE）的通用回归神经网络（GRNN）。将精英档案和精英交叉与人工免疫算法（AIA）相结合，生成AIAE-GRNN算法，以提高其适应性和准确性。在本文中，均方误差（MSE）被视为亲和函数。 AIAE用于优化GRNN的平滑因子；然后，求解最佳平滑因子，并将其代入训练后的GRNN。因此，对侵入数据进行了分类。本文选择了使用遗传算法（GA），粒子群优化（PSO）和模糊C均值聚类（FCM）分别进行了优化的GRNN，以便对这些方法进行比较。结果表明，AIAE-GRNN的分类精度高于PSO-GRNN，但AIAE-GRNN的运行时间较长，这首​​先被证明。 FCM和GA-GRNN由于在准确性和收敛性方面的缺陷而被淘汰。为了提高运行速度，本文采用主成分分析（PCA）来减少入侵数据的维数。随着维数的减小，PCA-AIAE-GRNN的精度下降幅度较小，收敛性优于PCA-PSO-GRNN，并且PCA-AIAE-GRNN的运行速度得到了较大的提高。实验结果表明，AIAE-GRNN具有比所考虑的其他算法更高的鲁棒性和准确性，因此可以用于对侵入数据进行分类。

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