The present invention discloses a malicious code analysis method based on semi-supervised learning. The method is characterized in that analysis is carried out based on the multi-dimensional features, and static features and dynamic features of a malicious code are extracted; and the difficulty of the subsequent processing through dimensionality reduction is reduced, and by using the semi-supervised learning method, the problem of the classification of a small number of marked malicious code samples is solved.

Malicious code analysis method and system based on semi-supervised learning

The invention discloses a detection and analysis method for an Android malicious application on the basis of deep learning. The method comprises the following steps that: on the basis of categories, extracting a malicious application feature set; adopting a deep learning algorithm to carry out malicious detection on an application sample, and classifying the application sample into malicious applications and normal applications on the basis of a detection result; adopting a feature selection algorithm to sort malicious application features; adopting a machine learning classification algorithm to identify the malicious family; and constructing a malicious family feature subset, and analyzing malicious family behaviors. By use of the method, the deep learning method is applied to Android malicious application detection, and the malicious family is subjected to behavior analysis through the construction of the feature subset. By use of the method, the classification accuracy of the Android malicious application can be improved, the problems of huge workload and low accuracy caused by the manual check of a current Android market are eliminated, and the method is favorable for adopting a targeted protective measure according to the behavior of the malicious application.

Detection and analysis method for Android malicious application on the basis of deep learning

The invention discloses a detection and analysis method of large-scale rapid mobile APPs The method includes: acquiring the mobile APPs from a mobile phone application market; acquiring natural language features of the APPs by using a natural language processing technique facing a backward parsing code; acquiring DEX image features of the APPs by using a feature image processing technique facingDEX; judging whether the APPs are repackaged applications or not by using a fuzzy Hash based repackage validation technique; if the APPs are judged to be malicious APPs, printing MALWARE labels on thenatural language features and the DEX image features of the APPs and placing natural language features and the DEX image features into a verification model of a machine learning module; if the APPs are judged to be normal APPs, printing NORMAL labels on the natural language features and the DEX image features of the APPs, and placing the natural language features and the DEX image features into the verification module of the machine learning module

Detection and analysis method of large-scale rapid mobile APPs

The invention discloses a sample prediction method and device based on machine learning, a server and a computer storage medium. The method comprises the steps that characteristics of each sample to be predicted in a to-be-predicted sample database are extracted to obtain characteristic vectors of the sample to be predicted; the characteristic vectors of each sample to be predicted in the to-be-predicted sample database are stored; a training model obtained through training is obtained, the training model is utilized to scan the characteristic vectors of each stored sample to be predicted, anda prediction result of each sample to be predicted is obtained. According to the scheme, by extracting the characteristic vectors of the samples to be predicted, the characteristic vectors extractedalready are scanned only by utilizing the training model during each time of prediction without the operation that full texts of the samples to be predicted are scanned by a new training model, and the characteristic vectors are extracted again. Therefore, the scanning and prediction speed can be greatly improved by adopting the scheme.

Sample prediction method and device based on machine learning and server

The invention relates to an Android malware detection method based on depth learning, belonging to the field of computer and information science and technology. The invention firstly extracts featuresof Android application software, and then extracts relevant security features by decompressing and decompressing Android application files. The extracted features include three aspects: file structure feature, security experience feature and N-Gram statistic characteristic. Then the extracted features are numerically processed to construct feature vectors. Finally, a DNN (Deep Neural Network) model is constructed based on the above extracted features. The new Android software is classified and identified by the constructed model. This method combines the analysis of instruction set and has the function of anti-malware confusion. At the same time, malware detection based on depth model can enhance the feature learning, can express the abundant information of big data, and can adapt to theevolving malware more easily.

An Android malware detection method based on depth learning

The invention discloses a Dalvik instruction abstraction-based Android malicious code detection method. The method comprises the following steps of 1) detecting a malicious code and training a classification model: extracting a Dalvik operation code from a smali file, performing abstraction simplification to obtain an instruction symbol, performing statistics and normalization processing on N-Gram sequence characteristics of the abstract Dalvik instruction symbol, and finally establishing a malicious code detection model and a malicious family classification model by adopting a machine learning-based classification algorithm; and 2) preprocessing a to-be-detected APK file firstly, extracting Dalvik instruction characteristics, performing abstraction simplification and N-Gram serialization processing, and preliminarily judging whether the code is a malicious code or not through detection of the malicious code detection model; and if not, directly giving a detection result, or if yes, obtaining a malicious code family type further through the malicious family classification model. The method is high in speed and relatively high in validity.

Dalvik instruction abstraction-based Android malicious code detection method

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Dalvik instruction abstraction-based Android malicious code detection method