TY - GEN
T1 - VPNDroid
T2 - 32nd International Conference on Artificial Neural Networks, ICANN 2023
AU - Polatidis, Nikolaos
AU - Pimenidis, Elias
AU - Trovati, Marcello
AU - Iliadis, Lazaros
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/9/22
Y1 - 2023/9/22
N2 - Protecting online privacy using Virtual Private Networks (VPNs) is not as simple as it seems, since many well-known VPNs may not be secure. Despite appearing to be secure on the surface, VPNs can be a complete privacy and security disaster by stealing bandwidth, infecting devices with malware, installing tracking libraries, stealing personal data, and leaving data exposed to third parties. Therefore, Android users must exercise caution when downloading and installing VPN software on their devices. To this end, this paper proposes a neural network combined with a random forest that identifies malicious and malware-infected VPNs based on app permissions, along with a novel dataset of malicious and benign Android VPNs. The experimental results demonstrate that our classifier achieves high accuracy and outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall.
AB - Protecting online privacy using Virtual Private Networks (VPNs) is not as simple as it seems, since many well-known VPNs may not be secure. Despite appearing to be secure on the surface, VPNs can be a complete privacy and security disaster by stealing bandwidth, infecting devices with malware, installing tracking libraries, stealing personal data, and leaving data exposed to third parties. Therefore, Android users must exercise caution when downloading and installing VPN software on their devices. To this end, this paper proposes a neural network combined with a random forest that identifies malicious and malware-infected VPNs based on app permissions, along with a novel dataset of malicious and benign Android VPNs. The experimental results demonstrate that our classifier achieves high accuracy and outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall.
KW - Android
KW - Convolutional Neural Networks
KW - Machine Learning
KW - Malware detection
KW - Permissions
KW - VPN
UR - http://www.scopus.com/inward/record.url?scp=85174602273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174602273&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-44204-9_37
DO - 10.1007/978-3-031-44204-9_37
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85174602273
SN - 9783031442032
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 444
EP - 453
BT - Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Angelov, Plamen
A2 - Jayne, Chrisina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 September 2023 through 29 September 2023
ER -