Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems
نویسندگان
چکیده
Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due their complex, dynamic, non-linear properties. One of most critical problems is attacks that damage infrastructure entire ITS. Attackers inject malware code triggers dangerous actions such as information theft unwanted system moves. The main objective this study improve performance detection models using Graph Attention Networks. To detect addressing ITS, a Network (GAN)-based framework proposed in study. inputs are Application Programming Interface (API)-call graphs obtained from benign Android apk files. During graph creation, network metrics Node2Vec model utilized generate node features. A GAN-based combined with different types features during experiments compared against Convolutional (GCN). Experimental results demonstrated integration GAN provides best terms F-measure accuracy parameters and, also, use an attention mechanism improves performance. Furthermore, generated resulted 3% increase classification metrics.
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ژورنال
عنوان ژورنال: Electronics
سال: 2021
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics10202534