Machine learning-based Android malicious program detection method

• Main Authors: A MANTAI, CUI YUANBO, JIN HONG, LIU CHANGYONG, NIE JIAHE, WANG YU, YANG MANZHI, HE CHUNFENG
• Format: Patent
• Language: Chinese; English
• Published: 20.07.2018
• Subjects: CALCULATING; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

The invention discloses a machine learning-based Android malicious program detection method. The method comprises the steps that feature extraction is conducted on black and white samples; a sample set is used for model training; to-be-detected samples are identified by a trained model; if the to-be-detected samples are identified as malicious samples, family classification is conducted on the samples, and if the to-be-detected samples are identified as white samples, abnormality detection is performed to determine whether or not the white samples are new malicious samples; a recognition result is fed back to a sample library for saving; a training set is added to a sample for identifying errors, and the model is retrained. According to the method, by adopting a machine learning algorithmand an online learning method, the problems of high missed detection rate and low identification accuracy of malicious programs in an existing detection method are solved. 本发明公开了种基于机器学习的安卓恶意程序检测方法，所述方法包括：对黑白样本进行特征提取；使用样本集进行模型