摘要
为了实现对电力系统未知恶意软件的准确检测,本文提出了一种基于深度置信网(DBN)的恶意软件检测系统。该系统将恶意软件解构为操作码序列,提取其中具有检测价值的特征向量,并使用DBN分类器实现恶意代码的分类。通过分类性能、特征提取和未标记数据训练的实验,证明了基于DBN的分类器能够使用未标记数据进行训练且具有优于其他分类算法的准确性,基于DBN的自动编码器可以有效地明显减小特征向量的维数。
In order to achieve accurate detection of unknown malware in power system,this paper proposes a malware detection system based on Deep Trusted Network(DBN).The system deconstructs the malware into an opcode sequence,extracts the feature vector with the detection value,and uses the DBN classifier to classify the malicious code.Through the experiments of classification performance,feature extraction and unlabeled data training,it is proved that DBN-based classifiers can use unlabeled data for training and have better accuracy than other classification algorithms.The DBN-based automatic encoder can effectively reduce the dimension of the feature vector significantly.
作者
葛朝强
葛敏辉
翟海保
张亮
GE Chao-qiang;GE Min-hui;ZHAI Hai-bao;ZHANG Liang(East China Branch,State Grid Corporation,Shanghai 200120 China)
出处
《自动化技术与应用》
2021年第4期62-67,共6页
Techniques of Automation and Applications
关键词
恶意软件检测
DBN
深度学习
信息安全
malware detection
DBN(Deep Belief Networks)
deep learning
information security
