Research on Android Malware Detection Methods Based on Pre-trained Language Models
In recent years,the pre-training learning algorithms in Android system have been greatly developed.However,limited by the difficulty of obtaining existing malicious code samples and the usually small set of labeled data,the promotion performance of the trained learning models is restricted.For this reason,this project proposes to study the malicious detection algorithms for programs based on pre-trained language models.Aiming at large-scale unlabeled APK data,an unsupervised approach is adopted to train it,and rich and complex semantic associations are extracted from the massive unlabeled APKs,so as to improve its promotion performance.Using the labeled malicious code samples,the language model is optimized so that it can detect virus codes more efficiently,thus ensuring the security of Android system operation.
pre-trained language modelAndroid malwaredetection methods
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