Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

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 to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that damage the infrastructure of the entire ITS. Attackers can inject malware code that triggers dangerous actions such as information theft and unwanted system moves. The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks. To detect malware attacks addressing ITS, a Graph Attention Network (GAN)-based framework is proposed in this study. The inputs to this framework are the Application Programming Interface (API)-call graphs obtained from malware and benign Android apk files. During the graph creation, network metrics and the Node2Vec model are utilized to generate the node features. A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN). Experimental results demonstrated that the integration of the GAN and Node2Vec models provides the best performance in terms of F-measure and accuracy parameters and, also, the use of an attention mechanism in GAN improves the performance. Furthermore, node features generated with Node2Vec resulted in a 3% increase in classification accuracy compared to the features generated with network metrics.

[1]  Ali A. Ghorbani,et al.  Android Botnets: What URLs are Telling Us , 2015, NSS.

[2]  Gaoxiang Zhang,et al.  Toward Secure Crowd Sensing in Vehicle-to-Everything Networks , 2018, IEEE Network.

[3]  Kandaraj Piamrat,et al.  Data Analysis for Self-Driving Vehicles in Intelligent Transportation Systems , 2020 .

[4]  Shaoyin Cheng,et al.  GDroid: Android malware detection and classification with graph convolutional network , 2021, Comput. Secur..

[5]  Si Zhang,et al.  Graph convolutional networks: a comprehensive review , 2019, Computational Social Networks.

[6]  Hakan Gunduz,et al.  An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson's disease classification , 2021, Biomed. Signal Process. Control..

[7]  Fouad Jawab,et al.  RESEARCHES AND APPLICATIONS OF INTELLIGENT TRANSPORTATIONS SYSTEMS IN URBAN AREA: SYSTEMATIC LITERATURE REVIEW , 2019 .

[8]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..

[9]  Seunghyun Park,et al.  Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms , 2020 .

[10]  Sakir Sezer,et al.  DL-Droid: Deep learning based android malware detection using real devices , 2019, Comput. Secur..

[11]  Hakan Gunduz,et al.  An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination , 2021, Financial Innovation.

[12]  Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges , 2020, Malware Analysis Using Artificial Intelligence and Deep Learning.

[13]  Arslan Munir,et al.  Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges , 2019, IEEE Intelligent Transportation Systems Magazine.

[14]  Sakir Sezer,et al.  DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection , 2019, IEEE Transactions on Cybernetics.

[15]  Yuan Yan Tang,et al.  Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification , 2019, IEEE Geoscience and Remote Sensing Letters.

[16]  Jan Eric Lenssen,et al.  Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.

[17]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Qian Luo,et al.  Wireless Telematics Systems in Emerging Intelligent and Connected Vehicles: Threats and Solutions , 2018, IEEE Wireless Communications.