With the widespread popularity of Internet of Things (IoT) devices based on the android system, the amount of androidmalware targeting IoT devices continues to increase, causing great economic losses. Accordingly, ef...
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With the widespread popularity of Internet of Things (IoT) devices based on the android system, the amount of androidmalware targeting IoT devices continues to increase, causing great economic losses. Accordingly, efficient and accurate androidmalwaredetection methods are particularly important. Recently, many android malware detection and classification methods have been proposed, but most of them ignore the deep relationships among software. In this article, we propose a graph compression algorithm with reachability relationship extraction (GCRR) and design an android malware detection and classification method called GCDroid based on this algorithm. A theoretical analysis shows that GCRR can reasonably extract the reachability relationships among APKs and compress a large heterogeneous APK-API relationship graph into a homogeneous APKs graph. Experiments show that GCDroid based on GCRR greatly reduces the required time consumption while improving detection accuracy. Compared with the existing excellent static androidmalwaredetection methods, GCDroid improves upon their detection accuracies by 1.53%-39.13% on different data sets and outperforms the benchmark methods in terms of androidmalwareclassification. Furthermore, compared with those of the baseline methods that are similar to GCDroid, GCDroid's time consumption for model training and other aspects is only one-tenth as high or even less.
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