摘要
现有的检测方法检测准确率相对较低,检测效果不理想。为此,文章研究基于模糊神经网络的软件运行漏洞智能检测方法,挖掘软件运行信息,提取数据特征;使用这些特征和对应的标签来构建模糊神经网络模型,调整内部参数,学习到全面的特征信息。结合PSO算法,文章将模糊化特征输入动态模糊神经网络模型中,计算最优解并将其转换成信号。通过分析信号与阈值关系,判断是否存在漏洞,从而完成检测。实验结果表明,算法的收敛速度较快,能够在较短时间内达到预期效果,收敛效果较好;实验组的检测准确率超过95%,大幅减少了对正常状态的误判,从而确保了更高的检测准确性。
Due to the relatively low existing detection method and detection accuracy,the detection effect is not ideal,so the intelligent detection method of software operation vulnerability based on fuzzy neural network is studied to mine the software operation information and extract the data features.These features and corresponding labels are used to construct fuzzy neural network models,adjust internal parameters,and learn comprehensive feature information.Combined with the PSO algorithm,the fuzzy features are input to the dynamic fuzzy neural network model and converted into signals.By analyzing the relationship between signal and threshold,it is judged that there are enough loopholes to complete the detection.The experimental results show that the convergence speed of the algorithm can achieve the expected effect in a short time,showing a better convergence effect;the detection accuracy of the experimental group exceeds 95%,greatly reducing the misjudgment of the normal state,thus ensuring the higher detection accuracy.
作者
魏小玉
于恩泽
WEI Xiaoyu;YU Enze(Information Engineering College,Zhengzhou Shengda University,Zhengzhou 450000,China)
出处
《无线互联科技》
2025年第8期84-87,共4页
Wireless Internet Science and Technology
关键词
模糊神经网络
软件运行
漏洞
智能检测
学习框架
fuzzy neural network
software operation
vulnerability
intelligent detection
learning framework
