卷积神经网络图像识别技术在入侵监测系统中的应用研究

摘要

为应对入侵手段复杂多样的安全形势,解决现有入侵监测技术成本高、适应性差的问题,设计研发一种成本低廉、兼容性好、方便拓展的入侵监测系统.应用开源深度机器学习框架TensorFLow实现基于卷积神经网络的图像识别算法,构建辅助安全系统,并在多种工况下对系统有效性进行验证.结果表明:系统能以较高准确率对入侵行为进行识别,在多分类情景下,随训练样本数量的增加,模型预测准确率得到提高,而收敛时间有所增加,开启GPU加速后缩短为原来的1/10.%In order to solve the complicated and diversified security situation of intrusion and to solve the prob-lem of high cost and poor adaptability of existing intrusion detection technology,an intrusion detection system with low cost,good compatibility and convenient expansion is designed and developed. The image recognition algorithm is realized based on convolution neural network using open-source depth machine learning frameworkTensorFLow,and the system architecture is constructed. Then the effectiveness of the system is validated under various operating conditions. Results show that the intrusion behavior can be identified by the system with highaccuracy. In a multi-class scenario,with the increase of the number of training samples,the prediction accuracy of the model is increased. While the convergence time is increased,the time is shorten to one tenth when the GPU acceleration is turned.