| Title | Android Malware Detection Based on Heterogeneous Information Network with Cross-Layer Features |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Xixuan, Ren, Lirui, Zhao, Kai, Wang, Zhixing, Xue, Anran, Hou, Qiao, Shao |
| Conference Name | 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) |
| Date Published | dec |
| Keywords | Android Malware Detection, codes, composability, compositionality, Cross layer design, Cross Layer Security, Cross-layer features, feature extraction, Heterogeneous information network (HIN), Internet of Things (IoT) Security, Java, Media, Operating systems, pubcrawl, resilience, Resiliency, Semantics |
| Abstract | As a mature and open mobile operating system, Android runs on many IoT devices, which has led to Android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based Android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection. |
| DOI | 10.1109/ICCWAMTIP56608.2022.10016587 |
| Citation Key | xixuan_android_2022 |
Android Malware Detection Based on Heterogeneous Information Network with Cross-Layer Features