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The behavior of malware threats is gradually increasing, heightened the needfor malware detection. However, existing malware detection methods only targetat the existing malicious samples, the detection of fresh malicious code andvariants of malicious code is limited. In this paper, we propose a novel schemethat detects malware and its variants efficiently. Based on the idea of thegenerative adversarial networks (GANs), we obtain the `true' sampledistribution that satisfies the characteristics of the real malware, use themto deceive the discriminator, thus achieve the defense against malicious codeattacks and improve malware detection. Firstly, a new Android malware APK toimage texture feature extraction segmentation method is proposed, which iscalled segment self-growing texture segmentation algorithm. Secondly, tensorsingular value decomposition (tSVD) based on the low-tubal rank transformsmalicious features with different sizes into a fixed third-order tensoruniformly, which is entered into the neural network for training and learning.Finally, a flexible Android malware detection model based on GANs with codetensor (MTFD-GANs) is proposed. Experiments show that the proposed model cangenerally surpass the traditional malware detection model, with a maximumimprovement efficiency of 41.6\%. At the same time, the newly generated samplesof the GANs generator greatly enrich the sample diversity. And retrainingmalware detector can effectively improve the detection efficiency androbustness of traditional models.

Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor