Supp o rt vector machine ( SVM ) has a good application in intrusion detection, but its performance needs to be further improved. This study mainly analyzed the optimization algorithm of SVM . F irst ly, the principle of SVM was introduced , then SVM was improved using w hale o ptimization a lgorithm (WOA) , the WOA was improved , the intrusion detection method based on IW OA -SVM was analyzed , and experiments were carried out on KDD CUP99 to verify the effectiveness of the algorithm . The results show ed that the IWAO-SVM algorithm was more accurate in attack detection ; compared with SVM, PSO-SVM and ACO-SVM algorithms , the performance of the IWAO-SVM algorithm wa s better, the detection rate was 99.89%, the precision rat io wa s 99.92%, the accuracy rate wa s 99.86%, and the detection time wa s 192 s, showing that it had high precision in intrusion detection. The experimental results verify the reliability of the IWAO-SVM algorithm , and it can be promoted and applied in the detection of network intrusion prevention.

中文翻译：

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基于Svm优化算法的网络入侵防御检测研究

支持向量机（SVM）在入侵检测方面有很好的应用，但其性能有待进一步提高。本研究主要分析了SVM的优化算法。首先介绍了SVM的原理，然后利用鲸鱼优化算法(WOA)对SVM进行了改进，对WOA进行了改进，分析了基于IW OA-SVM的入侵检测方法，并进行了实验。 KDD CUP99来验证算法的有效性。结果表明，IWAO-SVM算法在攻击检测上更准确；与 SVM、PSO-SVM 和 ACO-SVM 算法相比，IWAO-SVM 算法的性能更好，检测率为 99.89%，准确率为 99.92%，准确率为 99.86%，并且检测时间为 192 秒，表明它在入侵检测方面具有较高的精度。实验结果验证了IWAO-SVM算法的可靠性，可在网络入侵防御检测中推广应用。