Android malicious application detection based on Support Filtering and Lasso LR Algorithm

With the vigorous development of the mobile Internet, the Android system, which accounts for 76% of the drama mobile operating system, has also been widely promoted and popularized. However, due to its own open source characteristics, various parts of the Android system are facing serious threats from hacker attacks, and the main threat comes from malicious applications. In order to cope with the challenge, which Android malicious application variants emerging in endlessly and growth rapidly, this paper is based on machine learning algorithms and focuses on the research of malicious application detection algorithms under the Android platform. Based on that we proposes a lightweight Android malware detection and identification algorithm. Aiming at the requirement of lightweight model, In response to the needs of lightweight models, the feature selection method based on support filtering and Lasso LR model is adopted to greatly reduce the feature space. Combining the characteristics of high feature dimension, using the field-aware decomposition machine (FFM) model as the classifier, the detection performance with an F1 value of 0.990887 is achieved, and the accuracy of the detection of malicious applications is improved.