基于KNN 算法的计算机软件安全性检测技术研究

Research on Computer Software Security Detection Technology Based on KNN Algorithm

随着计算机软件的广泛应用，软件安全性问题日益突出，特别是面对不断增长的网络攻击和数据泄露风险。本研究旨在探索基于KNN 算法的计算机软件安全性检测技术，以提升对异常数据的准确检测能力。研究首先进行了数据预处理和特征提取，随后详细分析了KNN 算法的实施步骤和性能评估。通过在多个样本集上的实验测试，本文得出结论：基于KNN 算法的软件安全性检测技术能够有效降低误报率和漏报率，提升了软件安全检测的准确性和稳定性。

With the widespread use of computer software, software security issues have become increasingly prominent, especially in the face of growing risks from cyber-attacks and data breaches. This study aims to explore computer software security detection technology based on the K-Nearest Neighbors (KNN) algorithm to enhance the accuracy of detecting anomalous data. The research begins with data preprocessing and feature extraction, followed by a detailed analysis of the implementation steps and performance evaluation of the KNN algorithm. Through experimental testing on multiple datasets, the study concludes that software security detection technology based on the KNN algorithm effectively reduces false positive and false negative rates, thereby improving the accuracy and stability of software security detection.