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| 1 | Malware Detection Algorithm Based on the Attention Mechanism and ResNet显示文摘The cost of misclassifying a malware program as normal is often higher than that of misclassifying a normal program as malware.Therefore,how to improve the detection accuracy of malware programs is a very important problem.This paper proposes a deep learning malware program detection algorithm based on attention mechanism.Word2Vec model is used to map the Application programming interface(API)into word vectors,and all word vectors of each sample are arranged into a matrix with the same size.On this basis,residual network is used to extract features of samples.The features are input into the attention mechanism to learn the similarity between samples.Then,the features are weighted with the similarity to obtain the new features with better robustness.The new features and the original features are added element by element to obtain the sample features more suitable for classification.Finally,samples are classified by classifier.Experiments show that the classification effect of the proposed method is better than that of the traditional machine learning method. | WANG Lele WANG Binqiang ZHAO Peipei LIU Ruyi LIU Jiangang MIAO Qiguang | 2020 | Chinese Journal of Electronics2020，29，6： | 5 |
| 2 | 复杂场景下的猫眼目标快速识别方法显示文摘为了提高复杂场景中猫眼目标识别的准确率与算法实时性,结合传统图像处理方法与深度学习中的孪生网络相似度检测原理,通过对采集的主被动图像进行预处理,利用显著性检测进行猫眼目标增强,阈值分割提取疑似目标区域,采用综合形状度量以及孪生网络对候选目标区域进行联合识别提取真实猫眼目标。实验结果表明,该方法的准确率达到98.03%,虚警率为3.07%,检测速度快,可用于实时性检测。 | 陈文龙 张来线 孙华燕 郭惠超 王明乾 | 2022 | 兵器装备工程学报2022，43，7： | 1 |
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